WEBVTT
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Language: en
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Welcome you to all, to this course Digital
Human Modeling and Simulation for Virtual
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Ergonomic Evolution.
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Now, today is the second module, for with
to, use of percentile of anthropometric and
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bio-mechanical data for product or any other
facility design.
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Last, we discussed about introduction to ergonomics.
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Now, we are going to discuss about anthropometric
and biomechanical data and different types
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and anthropometric and biomechanical data,
how those data is used in different types
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of product or facility design.
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Now, this module 2, before going to discuss
about anthropometry, biomechanics or anthropometric
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and biomechanical data, it is better to study
little bit about basic statistics, which are
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very much essential to understand, what is
percentile?
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What is mean value?
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What is standard deviation?
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If you understand these basic concepts of
statistics, it will help us to understand
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that how percentile anthropometric data or
biomechanical data can be used for different
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types of facility design.
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So, first we start with percentage.
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So, what is percentage?
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All you of we know, in the class, student
today may ask you got 40 percent marks or
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80 percent marks then what does it mean?
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80 percent, you got 80 percent marks ,means,
out of 100 you got 80.
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If your exam is in 50 marks, then out of 50
you got 40 marks.
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If you calculated then what is out of 100
then, out of 100 is coming 80.
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So, percentage can generally be described
as or defined as a number or ratio expressed
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as the fraction of 100.
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Generally, it is denoted with this sign, percentage
sign.
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The word percent comes from Latin word called
‘per centum’, what does it mean?
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It means 100.
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So, already as I mentioned in this 1 way,
percentage is calculated.
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Now, you got out of 50, 40 marks and after
calculation we can say, you got 80 percent
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marks, but in your class, your marks is such
that your rank in the class is ,say, for example,
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I am telling 78 percentile.
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So, you got 80 percent marks, but your rank
in the class, based on the marks is 78 percentile.
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So, what does it mean, percentage is something
different and percentile is something.
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So, now to understand that, if we look at
the definition of the percentile, percentile
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is the value below which the certain percentage
of data falls ,say, for example, if we ask
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all the students to be in a queue, in their
height, ascending order of their height, then
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say this is you.
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So, you, your height, we consider, here it
is the marks, but if you consider it as height.
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If we consider that your height, say, for
example, 170 centimeter is such that your
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percentile value is coming to 70 centimeter,
78 percentile.
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What does it mean, it means below your height
value, how many other students are there?
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70 percent students are there and above your
height how many students are there?
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There are 30 percent students.
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Similarly, in case of marks also, you got
80 percent marks, that is out of 50, you got
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40 marks.
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It is, we are telling it is 78 percentile.
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What does it mean?
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It mean, you got, your marks is such that
it is higher than 70 percent student’s marks
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and it is lower than 30 percent student marks.
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Another example, if I mention that your percentile
or your rank per your score is 60th percentile,
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what does it mean?
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60th percentile ,means, below your marks,
how many student marks are there?
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Below your marks, another 60 percent students
got the marks below your mark.
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Similarly, above you, how many student’s
got marks, 40 percent student got more than
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you.
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So, that is why your position or rank in the
class is 60th percentile.
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So, in this way, percentage and percentile
is completely different, percentage when your
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marks is converted out of hundred that is
the percentage, but your rank while you are
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expressing in percentile, it means, if we
consider the marks as data and for all student
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marks if you tabulate in ascending order or
descending order then, say, someone got 40,
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someone 41, in this way someone ,say, out
of 50, but 49.
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In this way, if we arrange the marks in ascending
or descending order, so if we, put like this
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way.
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So, someone got 38, someone 39, someone 39.5,
40.
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In this way someone got 48, 49 someone got
42, 50.
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If this the scenario and I am telling, you
got 48 marks and this marks I am telling,
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if your rank is 90th percentile.
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You got 48 marks out of 50 and your rank in
the class is 98 percentile.
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What does it mean, how many, mean, another
10 percent student.
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Here are 10 percent students who got more
than you and here are 95 percent students
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who got less than your marks.
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So, 98 percentile means, any specific percentile,
percentage you got 48 marks and that marks
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is 90th percentile.
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It means 90 percent data, marks data is below
this 48 and 10 percent of the total data is
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above this 48 and those data actually corresponding
to students, means, 90 percent of student
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got less than your marks and 10 percent student
got more than your marks.
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Same thing can be, we can give the example
of height.
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So, this is the marks, similarly, if you discuss
the same thing with the height.
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So, in a class room there are so many students,
someone’s height is 140 centimeter.
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This is the height, height value expressed
in centimeter, 140 centimeter.
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So, 141 centimeter, 142 centimeter, 142.5
centimeter, 143 centimeter ,again, 143 centimeter,
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then this 145 centimeter, 148 centimeter,
160 centimeter, 180 centimeter, 178 centimeter.
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So, in this way we can put the height value
of all the students, starting from 140 centimeter
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and it is ending at 180 centimeters.
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If you measure the height of the all the students
and put those height values in ascending order
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then we are getting this type of data set.
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Out of the data set, assume this is your height,
142 centimeter, 142 centimeter is your height
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value and in this height data set, we are
telling this is such a data, this data is
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5th percentile.
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If we mention that in this data set 142 centimeter
is 5th percentile, it means for height, 5
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percent data are below this 142 centimeter
and 95 percent data are above this.
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So, 5 percent data are below this 142 centimeter
and 95 percent data is above this 142 centimeter.
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Similarly, if we mention your height is 148
centimeter and if I say, 177 meters.
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This is 1 height value, this height if we
mention, this is 95th percentile, if we mention
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177 centimeter, this is 95th percentile, what
does it mean, within this data set 177 centimeter
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is such a data above that five percent data
is there and below that 95 percent data is
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there.
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So, if we discuss about this one, that in
this data set, while it is arranged in ascending
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order, if we mention that 177 centimeter is
such a data, in this data set, if we call
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this is 95th percentile, it means, above this
how many data are there?, 5 percent of the
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data are there and below this value how many
data are there, 95 percent data are there,
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that is why it is 95th percentile.
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Now, how to calculate percentile.
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There are different methods.
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So, one way to calculate percentile value
from this frequency diagram.
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So, what is frequency diagram?
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So, within the height, height of a group,
particular group; height starting from 160
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centimeter and it is ending at 180 centimeter
and now we have divided in small interval
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160 to 162 centimeter, 162 to 164 centimeter.
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So, within this group, 160 centimeters to
162 centimeter, within this set, how many
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students are there?
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Say, 10 students are there.
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162 to 164 centimeter, how many students;
50 15 students are there.
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So, this is called frequency or number of
individual in that particular group.
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So, it is very clear, in this way the whole
range, we can divide into small intervals
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and within each internal how many members
are there we can tabulate it.
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So, that is frequency or number of individual
in the particular group.
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Now, what is cumulative frequency?
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What we present here, 160 to 162 centimeter,
within this range 10 people are there, but
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162 to 164 centimeter there are 15 individuals.
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But in case of cumulative frequency, it is
coming 25, how 10 plus 15 is equal to 25 , means,
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actual in this position what we you are writing.
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So, first one; 1 while we are talking about
cumulative frequency it means, it is actually
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below that 160.
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At 160, it is starting from 160 and ending
at 164 centimeter, within this range how many
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people are there?
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Total 10 plus 15, actually this 25 is coming
within the group room 160 centimeter to 164
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centimeter.
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Similarly, in this case, this is 17 for the
particular group range; 164 centimeter to
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160 centimeter, within this range there are
17 numbers of students and some number of
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individuals and, but the cumulative frequency
42, means, up to 166 centimeter, starting
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from 160 to 166 centimeter, within this range
how many students are there?
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That is 42, only mean, then we have to sum
of 10 plus 15 plus 17 that is coming to 42.
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So, in this way, in the cumulative frequency,
we have to add in each group whatever is the
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frequency or number of individual that we
have to sum up one after another.
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In this group 174, 172 to 174 centimeter,
within this range how many students are there?
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29 students are there, but what is the cumulative
frequency in that particular group?
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cumulative frequency is 136 centimeter, how
this 136 centimeter is coming, then we you
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have add, starting from 10 up to this 29 then
it is coming to 136 centimeter.
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So, 136 numbers of students actually belongs
to, in to that group starting from 160 centimeter
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and ending at 174 centimeter, it is starting
from 160 and ending at 174, within this range
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total number of students is 136.
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So, in this way cumulative frequency is calculated.
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Now, if we plot that cumulative frequency,
how it is plotted in the graph cumulative
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frequency diagram?
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In case of cumulative frequency diagram as
we mentioned, we have to sum up the frequency.
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So, 162 to 164 centimeter, within this range
how many students are there, that value we
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you have put over.
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So, 160 to 162 centimeter, how many 10; 160
to 162, we are putting 10 there, then next
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one is 162 to 164 centimeter, within this
it is 50, but cumulative frequency when we
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are talking about, then that is 25.
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So, we have to put here 25, next 42, in this
way whatever cumulative frequencies are there
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because on the x-axis, we are putting the
height and on y-axis we are putting the cumulative
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frequency.
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So, whatever, which are the cumulative frequency,
in the particular, against we kept, against
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that particular group you we have to put here.
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So, in this way in different, this small intervals,
whatever intervals are given, within each
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interval we are putting cumulative frequency,
then you we are getting this type of graph.
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So, here how many, total 200 students are
there.
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200 students’ data we can plot like this
way.
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Now, in this plot what is happening?
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So, 200 students will, total 100 percent students.
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Now, for particular, say, 150 numbers of students,
150 numbers of students is coming like this
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and it is intersecting at this point of the
graph and it will come there then you we are
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getting the value.
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So, if 200 is 100, 200 students is considered
as the 100 percent students.
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Then 150 means 70 percent, 100 means 50 percent,
50 means 25 percent.
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So, 100, we are considering it 100.
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So, 100 number of student, 100 students will
50 percent students.
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So, because total students 200, 50 percent
students, the height value is below this value,
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that is, we can easily, from this graph, we
can identify, what is the height value on
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the x-axis in the table.
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In that way you can mention that value is
coming to 171.5 centimeter.
00:18:18.610 --> 00:18:21.610
So, below this 171.5 centimeter, how many
students are there?
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100 students are there, that 100 students
actually refers 50 percent of the student
00:18:31.130 --> 00:18:32.130
population.
00:18:32.130 --> 00:18:39.760
So, this value 171.5 that you we can call
50th percentile, so, in this we can calculate
00:18:39.760 --> 00:18:41.360
the 50th percentile value.
00:18:41.360 --> 00:18:49.850
So, 171.5 centimeters, this is the value in
the data set, below that data, how many students
00:18:49.850 --> 00:18:50.850
are there?
00:18:50.850 --> 00:18:54.910
100 students are there, 100 student means
50 percent students, because total students
00:18:54.910 --> 00:18:55.910
are 200.
00:18:55.910 --> 00:19:00.390
Similarly, say if we concentrate on this 50
number of students, 50 number of student means
00:19:00.390 --> 00:19:03.100
20 percent students, 25 percent students.
00:19:03.100 --> 00:19:07.150
Out of 250 200; fifty students means 25 percent
student.
00:19:07.150 --> 00:19:13.220
Those 25 percent student’s height value
is actually below this point, what is this
00:19:13.220 --> 00:19:16.220
point, this point is 168.84 centimeter.
00:19:16.220 --> 00:19:26.210
So, 168.84 is such a height value in the data
set, below this value, 50 number of students,
00:19:26.210 --> 00:19:33.330
means, 25 percent students are there, 25 percent
of students are there ,mean, this value you
00:19:33.330 --> 00:19:39.160
we can mention as 25th percentile student,
below this 25th percentile height value.
00:19:39.160 --> 00:19:45.160
So, 25th percentile height value is 168.84
centimeter ,means, below this value, 25 percent
00:19:45.160 --> 00:19:53.930
students ,means, 50 students are there and
above this how many students are there, above
00:19:53.930 --> 00:20:01.910
this height value remaining portion, means,
remaining 75 percent students are there.
00:20:01.910 --> 00:20:06.990
Now, again we are going back to the definition
of the percentile.
00:20:06.990 --> 00:20:07.990
So, what is percentile?
00:20:07.990 --> 00:20:13.690
So, percentile, if we mention 78th percentile,
78 percentile means this is in a data set,
00:20:13.690 --> 00:20:22.730
this is the specific value, below that value
70 percent data are there, above that 30 percent
00:20:22.730 --> 00:20:24.580
data are there.
00:20:24.580 --> 00:20:32.030
Now, how it we can make a generalized definition;
percentile can be defined as a particular
00:20:32.030 --> 00:20:41.320
point in the data set, below that certain
percentage, In that example 70 percent, certain
00:20:41.320 --> 00:20:52.630
percentage of data exists and above that remaining
percentage of data exists, but the condition
00:20:52.630 --> 00:21:04.010
is that, we have to arrange the data in ascending
order.
00:21:04.010 --> 00:21:10.860
So, while we are arranging the data, as in
this case also, we have showed.
00:21:10.860 --> 00:21:16.830
While we are arranging the data in ascending
order, any percentile ,mean, below the particular
00:21:16.830 --> 00:21:21.720
percent, particular point, 142 centimeter,
5th percentile below this percentile value,
00:21:21.720 --> 00:21:27.250
5 percent data set are there, above this 51,
142 centimeter, which percentage of data is
00:21:27.250 --> 00:21:30.670
there, 95 percent data is there.
00:21:30.670 --> 00:21:32.950
So, what is percentage?
00:21:32.950 --> 00:21:33.950
What is percentile?
00:21:33.950 --> 00:21:41.080
So, percentile is a specific position or specific
point in the data set, below that certain
00:21:41.080 --> 00:21:50.940
percentage of the data exists and above that
remaining percentage of the data exists.
00:21:50.940 --> 00:21:58.190
So, percentile is not any individual or is
not any, say, height, this is the height value.
00:21:58.190 --> 00:22:07.390
So, height is not that percentile or that
student is not the, this 5th percentile height,
00:22:07.390 --> 00:22:10.380
mean, percentile is used for the data.
00:22:10.380 --> 00:22:16.250
So, his height value is the 5th percentile,
neither the student is 5th percentile neither
00:22:16.250 --> 00:22:24.100
the height is 5th percentile that height value
in the data set is 5th percentile, his height
00:22:24.100 --> 00:22:32.120
value 142 centimeter is the specific data
which we can mention as 5th percentile for
00:22:32.120 --> 00:22:35.470
the data set.
00:22:35.470 --> 00:22:44.710
Now, moving forward, then to few other anthropometric
parameters as statistical ,sorry, statistical
00:22:44.710 --> 00:22:49.650
parameter which are also important for understanding
the anthropometric and percentile calculation
00:22:49.650 --> 00:22:56.690
of anthropometric and biomechanical data is
mean, median mode.
00:22:56.690 --> 00:22:57.910
So, mean, median, mode, these are called central
tendency of the data.
00:22:57.910 --> 00:23:12.290
So, what is mean, all of we know, mean is
the statistical, meaningful, how we can define
00:23:12.290 --> 00:23:13.290
mean?
00:23:13.290 --> 00:23:21.480
Mean is the statistical parameter, implies
arithmetic average or arithmetic mean, which
00:23:21.480 --> 00:23:28.040
is obtained by summing up all the observations
and dividing the total by the number of the
00:23:28.040 --> 00:23:29.200
observation.
00:23:29.200 --> 00:23:32.090
So, this is expressed as like this.
00:23:32.090 --> 00:23:37.740
So, x is the individual observation, all the
individual observation we have to sum up then
00:23:37.740 --> 00:23:46.070
we have to divide by ‘n’ is the number
of, total number of observations, for example,
00:23:46.070 --> 00:23:52.090
if we give an example of a data set, where
these data are there 2, 5, 8, 6 in this way
00:23:52.090 --> 00:23:53.340
data are there.
00:23:53.340 --> 00:23:59.000
Then how we can calculate arithmetic average
or mean, then we have to sum up all the values
00:23:59.000 --> 00:24:03.850
and then we have to divide by the total number
of data points in the data sets.
00:24:03.850 --> 00:24:07.400
So, in this data set, there are total 13 data.
00:24:07.400 --> 00:24:13.020
So, you we have to divide it by 13, then we
are getting the mean value or average value
00:24:13.020 --> 00:24:14.660
for the data set.
00:24:14.660 --> 00:24:26.130
Now, next is the median, this is also another
central tendency of the data or data set ,what
00:24:26.130 --> 00:24:28.360
is median?
00:24:28.360 --> 00:24:32.241
Median is the mid-value.
00:24:32.241 --> 00:24:36.080
This statistical parameter implies the mid
value or middle observation, while the data
00:24:36.080 --> 00:24:37.900
is arranged in ascending or descending order.
00:24:37.900 --> 00:24:48.440
The same data set if we you consider and then
if we arrange them in ascending order, from
00:24:48.440 --> 00:24:54.230
lower value to higher value, then whatever
is the mid value, that is called the median.
00:24:54.230 --> 00:25:01.630
So, here we have arranged, we can find, this
side there are 1, 2, 3, 4 total 6 number of
00:25:01.630 --> 00:25:07.000
data points are there, above this value there
are total 6 number of data points.
00:25:07.000 --> 00:25:09.170
So, mid value is the 5.
00:25:09.170 --> 00:25:15.970
So, for this data set 5 is the median.
00:25:15.970 --> 00:25:24.581
Next mode, there is another parameter for
central tendency that is the mode.
00:25:24.581 --> 00:25:29.549
This statistical mode parameter implies the
most frequently occurring value in the data
00:25:29.549 --> 00:25:35.980
set, that which, in the data set any particular
value, which is coming for more number of
00:25:35.980 --> 00:25:38.530
times, that is called mode.
00:25:38.530 --> 00:25:47.140
With the same data set example, we can mention
that in the data set we can find, 5 is coming
00:25:47.140 --> 00:25:48.640
for how many times?
00:25:48.640 --> 00:25:54.890
5 is coming for 1, 2, 3; 3 times, 5 is coming
3 times.
00:25:54.890 --> 00:26:00.800
So, 5 is the mode, for median also 5 and mode
is in this case mode is also 5.
00:26:00.800 --> 00:26:10.980
Because in the data set 5 is coming for 3
times, now one important thing here, generally
00:26:10.980 --> 00:26:17.100
for all are the everyday purpose, we use arithmetic
average or mean, then what is the importance
00:26:17.100 --> 00:26:19.670
of mode or median?
00:26:19.670 --> 00:26:25.970
There are some situation, where median maybe
more expressive for central tendency than
00:26:25.970 --> 00:26:36.100
mean because the central value or the mid-value
for the data set may be expressed more by
00:26:36.100 --> 00:26:43.480
median than mean, when it happens, see in
the data set, if few values are too small
00:26:43.480 --> 00:26:46.350
or too high.
00:26:46.350 --> 00:26:52.280
In this example, if few data, in general it
is starting from 140 and gradually increasing
00:26:52.280 --> 00:27:01.511
to 180 centimeter, but at this case at one
end, either the bottom side or top side we
00:27:01.511 --> 00:27:09.540
will find if 1 or 2 values are very small
or large.
00:27:09.540 --> 00:27:17.860
If 1 or 2 values at the two ends are very
smaller or very larger, then what will happen,
00:27:17.860 --> 00:27:23.850
due to presence of those data, the average
value or the arithmetic mean, actually does
00:27:23.850 --> 00:27:34.700
not show the central tendency, in that case,
median will be the better representative of
00:27:34.700 --> 00:27:37.080
central tendency.
00:27:37.080 --> 00:27:46.690
So, in the data set where one or more number
of the values, very small or very large values
00:27:46.690 --> 00:27:52.140
are there in the data set, which actually
deviating, or which actually effecting the
00:27:52.140 --> 00:28:02.669
mean value, due to presence of the that values,
at the lower end or higher end, if it is effecting
00:28:02.669 --> 00:28:11.080
the mean, in the scenario median may the better
representative.
00:28:11.080 --> 00:28:18.760
Now, next important statistical parameter
is standard deviation.
00:28:18.760 --> 00:28:30.390
We are coming to this example, say, this is
also height; students height in centimeter.
00:28:30.390 --> 00:28:37.559
It is starting from 140 centimeter and ending
at 171 centimeter, we are assuming.
00:28:37.559 --> 00:28:45.650
So, sum is this and mean value this much and
we have calculated standard deviation.
00:28:45.650 --> 00:28:48.200
So, mean value is 155 centimeter.
00:28:48.200 --> 00:28:53.770
So, its mean value position is somewhere,
here in between 155 and 157.
00:28:53.770 --> 00:28:59.320
So, mean value actually lies in between these
2 values.
00:28:59.320 --> 00:29:05.179
So, all the data, in the data set are arranged
in ascending order.
00:29:05.179 --> 00:29:17.190
Now, if we look at the mean value, 155.8 centimeter,
from that mean value these 152 centimeter,
00:29:17.190 --> 00:29:26.750
there are some distance, few values are nearby,
150 is nearby, 154 is going away, 153, 152
00:29:26.750 --> 00:29:32.170
in this way it is gradually the distance is
increasing, 140.
00:29:32.170 --> 00:29:39.740
Similarly, this side also from 155, 160, 161
gradually the distance is increasing.
00:29:39.740 --> 00:29:45.210
So, actually mean is clear, mean is the arithmetic
average.
00:29:45.210 --> 00:29:46.710
Now, what is standard deviation?
00:29:46.710 --> 00:29:57.020
So, standard deviation actually indicates
how all these data points are dispersed or
00:29:57.020 --> 00:30:00.230
scattered around this mean value.
00:30:00.230 --> 00:30:06.230
If this is the mean value, from that mean
value, how all other distances are away from,
00:30:06.230 --> 00:30:12.870
whether these values are nearby mean value
or away from the mean value.
00:30:12.870 --> 00:30:19.430
So, for that purpose, how we can calculate
this standard deviation?
00:30:19.430 --> 00:30:23.980
So, standard deviation is calculated with
some formula, what we do, first we calculate
00:30:23.980 --> 00:30:30.430
the mean value, this is the mean value, then
from the mean value we make the difference.
00:30:30.430 --> 00:30:35.559
So, make the difference from, mean minus individual
observation.
00:30:35.559 --> 00:30:42.690
So, 155 minus 155, 155.8 minus 155, then again
we can make this type, 155.8 minus 153.
00:30:42.690 --> 00:30:53.890
In this way we can make, calculate the difference,
first we have to calculate the mean.
00:30:53.890 --> 00:31:02.200
Secondly, we have to calculate the difference
of individual observation from the mean.
00:31:02.200 --> 00:31:08.700
Now, if you look at this side and these values
are lower than 155.8 centimeter.
00:31:08.700 --> 00:31:15.790
So, whenever we will minus all these individual
values from 155.8 centimeter, it is giving
00:31:15.790 --> 00:31:17.610
positive value, all the differences are positive.
00:31:17.610 --> 00:31:30.270
But on the other hand, while we are subtracting
155 minus 158, then we are getting negative
00:31:30.270 --> 00:31:31.270
value.
00:31:31.270 --> 00:31:36.809
So, this side while we are making the difference
between 155 centimeter and individual values,
00:31:36.809 --> 00:31:43.400
then what is, this side, the differences from
the this individual value, those values difference
00:31:43.400 --> 00:31:47.690
from the mean value, is coming negative.
00:31:47.690 --> 00:31:54.910
So, for this purpose, what we do, make all
these negative value, to convert it positive,
00:31:54.910 --> 00:31:59.490
at the same this side is already positive.
00:31:59.490 --> 00:32:06.950
So, what we do, we add the, then we go for
square the difference of the observation.
00:32:06.950 --> 00:32:13.450
So, first we calculate the mean, then we calculate
the difference between mean value and individual
00:32:13.450 --> 00:32:18.790
observations, whatever difference you are
getting, then you have to go for, we have
00:32:18.790 --> 00:32:23.370
to make the square of that value, then all,
this side value as well as this side value
00:32:23.370 --> 00:32:28.190
will come positive, then we have to sum up,
all the differences we have to sum up after
00:32:28.190 --> 00:32:29.190
squaring.
00:32:29.190 --> 00:32:37.799
So, add the squared values to get the sum
of squares of deviations.
00:32:37.799 --> 00:32:43.160
Then you when we are getting the squared value,
then divide the sum by the number of observation
00:32:43.160 --> 00:32:45.480
minus 1, to get the mean-square deviation.
00:32:45.480 --> 00:32:54.470
Now, if their total and this is the sample,
in this sample how many values are there.
00:32:54.470 --> 00:33:01.190
Say, for example, here 20 values are there,
means, 20 data are there, out of the 20 data
00:33:01.190 --> 00:33:10.750
where, then their degree of freedom is, the
degree of freedom means, but now one important
00:33:10.750 --> 00:33:12.080
point is degree of freedom, degree of freedom
means, in a data set, how value can be changed
00:33:12.080 --> 00:33:20.860
or the data point can be changed other than
that particular value, if the total number
00:33:20.860 --> 00:33:27.370
of data in a data set is 20 trying to be.
00:33:27.370 --> 00:33:28.740
So, for the particular position or particular
value, each value can be changed to another
00:33:28.740 --> 00:33:29.740
nineteen values.
00:33:29.740 --> 00:33:31.830
So, that is why, for that particular value
total degree of freedom is 19.
00:33:31.830 --> 00:33:40.070
So, in that way curve what is for this sample,
if you we assume, that there are total ‘n’
00:33:40.070 --> 00:33:51.410
number of data points, then degree of freedom
for that data set is ‘n’ minus 1.
00:33:51.410 --> 00:34:00.530
We have to divide that squared sum some with
the ‘n’ minus 1 , that is the degree of
00:34:00.530 --> 00:34:06.140
freedom, then as you have already seen mean,
if ‘n’ is, here ‘n’ is the.
00:34:06.140 --> 00:34:09.869
Now, we are coming to the statistical expression.
00:34:09.869 --> 00:34:13.220
In the statistical expression, what we can
see, ‘n’ is the mean value, mean value,
00:34:13.220 --> 00:34:17.190
this is the mean value minus ‘x’ is the
individual observation, either this side or
00:34:17.190 --> 00:34:19.240
that side, all these individual observations.
00:34:19.240 --> 00:34:24.970
This may come positive or negative, for that
purpose we are making, we are putting square
00:34:24.970 --> 00:34:29.100
in all the differences in positive form.
00:34:29.100 --> 00:34:34.370
When it is coming in positive, then we are
dividing it by ‘n’ minus 1 , total number
00:34:34.370 --> 00:34:41.090
of observation minus 1 ,as the degree of freedom,
but if this is the sample data, then if it
00:34:41.090 --> 00:34:46.530
is the population data or very big data, number
of sample size is very high or very big sample
00:34:46.530 --> 00:34:54.790
size or population data, in that case again
instead of ‘n’ minus 1 ,we can use ‘n’.
00:34:54.790 --> 00:35:04.010
Similarly, here we are using, as it is a small
sample, we are using ‘n’ minus 1 ,then
00:35:04.010 --> 00:35:11.640
as initially for making all the difference
which were in negative or either in positive,
00:35:11.640 --> 00:35:14.380
in positive mode, we squared it.
00:35:14.380 --> 00:35:21.770
Now, as we put the square, now again we are
putting the square root for the whole expression,
00:35:21.770 --> 00:35:30.830
then it is given actually one expression,
that is expressed in, that how all other values
00:35:30.830 --> 00:35:33.580
in the data set are dispersed for mean value.
00:35:33.580 --> 00:35:41.080
So, mean value minus individual value then
square that one, after squaring divide it.
00:35:41.080 --> 00:35:47.010
So, that after squaring whatever and sum up
and whatever value is coming that is happening
00:35:47.010 --> 00:35:55.330
for ‘n’ number of data, then to get the
average value, we are diving that expression
00:35:55.330 --> 00:36:03.020
‘n’ minus ‘x’ square divided by ‘n’
minus 1 ,then you we are getting the average
00:36:03.020 --> 00:36:04.020
value.
00:36:04.020 --> 00:36:11.311
After getting the average value, as we put
that square, now again we are putting the
00:36:11.311 --> 00:36:12.311
square root.
00:36:12.311 --> 00:36:13.630
So, ultimately standard deviation is, has
statistical expression which is expressing
00:36:13.630 --> 00:36:21.050
that how, while the data arranged in ascending
order or descending, it may be also descending
00:36:21.050 --> 00:36:27.490
order, while the data is arranged in the ascending
order/descending order, then from the mean
00:36:27.490 --> 00:36:36.180
value, how all other values are arranged,
dispersed around that mean value.
00:36:36.180 --> 00:36:48.030
Now, this is very important ,the, if the mean
value of 2 different data sets are equal;
00:36:48.030 --> 00:36:54.670
data with the lower standard deviation value
indicates that, data, in that data set, are
00:36:54.670 --> 00:37:02.374
less scattered or less dispersed around the
mean, in comparison to another data set.
00:37:02.374 --> 00:37:04.910
We are giving that example, this data set,
two data sets are there.
00:37:04.910 --> 00:37:08.180
So, this is the 1 data set and this is the
mean value.
00:37:08.180 --> 00:37:15.650
Now as we mentioned, what is standard deviation,
we mentioned, standard deviation expresses
00:37:15.650 --> 00:37:20.220
how all other values in the data set are dispersed
along around the mean value.
00:37:20.220 --> 00:37:26.060
From this mean value, few data are nearby,
few data away from the mean.
00:37:26.060 --> 00:37:32.830
So, it is like this, some value are nearby,
some value are away.
00:37:32.830 --> 00:37:42.650
So, ultimately, say is for this data set,
mean value is coming to ,say, 40 and standard
00:37:42.650 --> 00:37:55.900
deviation is coming to 5, another data set,
where mean is the same, mean value is same
00:37:55.900 --> 00:38:04.580
in this case also, mean value is 40, but standard
deviation value ,mean, in how all other values
00:38:04.580 --> 00:38:06.840
are dispersed around on the mean value.
00:38:06.840 --> 00:38:13.210
If we calculate that one, then the standard
deviation is coming to 2, in both the cases
00:38:13.210 --> 00:38:19.080
mean value is same 40-40, but in this case
standard deviation is 2, in this case standard
00:38:19.080 --> 00:38:20.250
deviation is 5.
00:38:20.250 --> 00:38:26.920
So, what does it mean; in this case all values
are more dispersed on around the mean.
00:38:26.920 --> 00:38:33.500
In this case values are nearby mean value,that
is why standard deviation is less.
00:38:33.500 --> 00:38:41.660
So, standard deviation actually expresses
how all other data in the data set are dispersed
00:38:41.660 --> 00:38:46.980
around on the mean value, if those are nearby
mean value, then those are nearby mean values,
00:38:46.980 --> 00:38:48.450
then standard deviation value is less.
00:38:48.450 --> 00:38:54.020
If those data points are dispersed on around
the mean value or away from the mean value,
00:38:54.020 --> 00:39:10.599
then it is coming, then standard deviation
value is more.
00:39:10.599 --> 00:39:29.030
Now, next is normal distribution and normal
curve.
00:39:29.030 --> 00:39:38.280
So, normal distribution is one type of standard
distribution which occurs in our nature.
00:39:38.280 --> 00:39:43.830
So, generally, as we are discussing about
frequency diagram.
00:39:43.830 --> 00:39:50.960
Similarly, if we look at this table then it
will be easier to understand what is normal
00:39:50.960 --> 00:39:51.960
distribution.
00:39:51.960 --> 00:39:57.070
See if the, but first we need to know, why
we find this normal distribution.
00:39:57.070 --> 00:40:01.530
Generally, while you talk about normal distribution,
we mention, we will get this type of bell
00:40:01.530 --> 00:40:03.290
shaped curve, but when we get?
00:40:03.290 --> 00:40:12.050
When the data collection is random, sample
size is big, random data collection is there
00:40:12.050 --> 00:40:18.609
and then you we are getting this type of bell
shaped curve, then we can mention that data
00:40:18.609 --> 00:40:25.010
is following normal distribution pattern So,
how does this it happen?
00:40:25.010 --> 00:40:36.420
See, if we consider the height value for a
particular group of students or a particular
00:40:36.420 --> 00:40:37.420
population.
00:40:37.420 --> 00:40:42.330
So, for example, 142.5, below this how many
students are there?
00:40:42.330 --> 00:40:47.990
Three; 145.5 to 147.5 how many?
00:40:47.990 --> 00:40:50.849
Below 148 centimeter?
00:40:50.849 --> 00:40:53.720
145 centimeter?; Eight.
00:40:53.720 --> 00:41:11.100
Then below 145, next is, this frequency for
each group, 145 to 145.5 or 147.5 how many
00:41:11.100 --> 00:41:12.100
students are there?
00:41:12.100 --> 00:41:13.100
Fifteen.
00:41:13.100 --> 00:41:19.890
So, in this way with the small interval in
each group, number of students is given here,
00:41:19.890 --> 00:41:27.170
then what you are getting, at the below range
3, 8, gradually it is increasing and at the
00:41:27.170 --> 00:41:35.090
end also, middle its values more, at the end
it is again reducing.
00:41:35.090 --> 00:41:41.150
Even in any population, even, for the example
of the big class room, if we find how many
00:41:41.150 --> 00:41:45.670
students are very tall, number is very less,
one or two.
00:41:45.670 --> 00:41:55.560
Similarly, how many students in a class are
very smaller in size, answer is very limited,
00:41:55.560 --> 00:41:56.560
one or two.
00:41:56.560 --> 00:42:02.720
But more number of students with average body
dimensions is are found.
00:42:02.720 --> 00:42:11.670
So, further purpose, if we put this type,
say, 48 for x-axis.
00:42:11.670 --> 00:42:18.980
If you look at this graph on x-axis, this
is the height value and this is the small
00:42:18.980 --> 00:42:23.800
interval 142.5 centimeter, 140 to 145 centimeter
then 147.5 centimeter.
00:42:23.800 --> 00:42:29.470
So, within this each interval, how many students
are there?
00:42:29.470 --> 00:42:37.060
At the smaller, lower data range 142, below
142.5 centimeter only three students are there.
00:42:37.060 --> 00:42:43.900
142.5 to 145 centimeter, how many students
are there, eight students are there.
00:42:43.900 --> 00:42:49.750
145.5 to 147 centimeter, fifteen students
are there.
00:42:49.750 --> 00:42:58.839
So, in that way, we can see while we are going
towards the mid range, the number of students
00:42:58.839 --> 00:43:01.060
in that particular group is gradually increasing.
00:43:01.060 --> 00:43:08.609
Again, from the mid range, while we are moving
towards the higher range, higher values of
00:43:08.609 --> 00:43:11.350
the height, then number of students in a particular
group is reducing.
00:43:11.350 --> 00:43:14.470
So, as I mentioned.
00:43:14.470 --> 00:43:22.200
So, this is the height value on x-axis, we
said the height value and y-axis we have kept
00:43:22.200 --> 00:43:26.390
the number of students in that particular
group, that is the frequency.
00:43:26.390 --> 00:43:35.270
So, number of the students in the mid range
157, 157.5 or 160 centimeter, in that range
00:43:35.270 --> 00:43:43.089
the number of students are more, but while
we are going to the lower side or we are going
00:43:43.089 --> 00:43:47.360
to the higher side of the height value, the
number of students in that particular group,
00:43:47.360 --> 00:43:53.660
it is reducing, ultimately you are getting
this type of bell shaped curve.
00:43:53.660 --> 00:44:02.000
So, this bell shaped curve is called normal
distribution or normal curve, while the data
00:44:02.000 --> 00:44:07.330
set for follow the normal distribution, then
we will get this type of normal curve.
00:44:07.330 --> 00:44:10.080
So, few characteristics are mentioned here.
00:44:10.080 --> 00:44:21.900
So, that should be bell shaped, it is generally,
this type of curve is observed, then symmetrical
00:44:21.900 --> 00:44:27.050
from the mid line, both side are similar,
mean, median and mode.
00:44:27.050 --> 00:44:33.050
If any data set follow normal distribution
pattern, then this very important observation
00:44:33.050 --> 00:44:41.950
that mean, median and mode everything coincide
and the curve changes from the center convexity
00:44:41.950 --> 00:44:57.839
towards concavity at the both end; lower end
as well as upper end.
00:44:57.839 --> 00:45:14.280
Now, non-normal distribution and range and
what is the relation with percentile.
00:45:14.280 --> 00:45:23.990
So, in this graph, on x-axis we are keeping
height in centimeter and on y-axis this is
00:45:23.990 --> 00:45:37.320
the frequency.
00:45:37.320 --> 00:46:41.599
Now, if we draw the same thing here, putting
height in centimeter on x-axis and on y-axis,
00:46:41.599 --> 00:46:51.270
we are putting the frequency, frequency in
number of students in that particular group.
00:46:51.270 --> 00:47:01.000
Now, for a particular class, say, is starting
from 140 to 142 centimeter, within this group,
00:47:01.000 --> 00:47:02.220
how many students are there?
00:47:02.220 --> 00:47:04.500
Only one or two students are there.
00:47:04.500 --> 00:47:22.100
So, this side number of students; total 50
students are there, this is 25, this is 20,
00:47:22.100 --> 00:47:24.920
this is 10, this is,say, 30, 40, 50.
00:47:24.920 --> 00:47:29.810
So, 140 to 142 centimeters, how many students
are there?
00:47:29.810 --> 00:47:33.170
Only 2, 3 students are there.
00:47:33.170 --> 00:47:37.540
In the next group, how many students are there?
00:47:37.540 --> 00:47:46.720
The number is more, 140 to 144 centimeter,
similarly in this way while will we are coming
00:47:46.720 --> 00:47:54.220
to mid range, say 160 centimeter, then number
of students, 162 centimeter, within that range
00:47:54.220 --> 00:47:58.820
number of students are more, how many students
are there say, 40 students are there in that
00:47:58.820 --> 00:47:59.820
range.
00:47:59.820 --> 00:48:12.150
Similarly, while we are going to upper side,
say, 180 centimeter; to 182 centimeter, within
00:48:12.150 --> 00:48:15.180
that range again number of students are less.
00:48:15.180 --> 00:48:24.990
In between, say, 172 to 174 centimeter within
that range, number of students are relatively
00:48:24.990 --> 00:48:25.990
more.
00:48:25.990 --> 00:48:30.070
Similarly, here number will be more.
00:48:30.070 --> 00:48:43.349
So, ultimately we are getting this type of
bell shaped curve because students with average
00:48:43.349 --> 00:48:47.600
body dimension or average height value or
any other body dimension, their number is
00:48:47.600 --> 00:48:55.700
more, students with lower body dimensional
value, their number is less, similarly students
00:48:55.700 --> 00:48:59.170
with higher body dimensional value, their
number is also less.
00:48:59.170 --> 00:49:02.390
So, we get this type of bell shaped curve.
00:49:02.390 --> 00:49:12.020
Now for this data set or for these students,
we are assuming mean value ,say, 160 centimeter
00:49:12.020 --> 00:49:19.390
and standard deviation value is ,say, 3 centimeter.
00:49:19.390 --> 00:49:31.250
So, for a particular population, if the mean
value is 160 centimeter, this is the mean
00:49:31.250 --> 00:49:36.840
value, 160 centimeter and standard deviation
value is 3.
00:49:36.840 --> 00:49:43.290
Now, from the statistical analysis, while
any data set follow for the normal distribution,
00:49:43.290 --> 00:49:52.349
then it is observed that, as it is mentioned
here, mean plus one standard deviation value
00:49:52.349 --> 00:50:00.270
covers 68 percent of the population ,means,
from the mean value, if this the mean value
00:50:00.270 --> 00:50:09.300
point, from that 160 centimeter mean plus
one standard deviation value power 68 percent
00:50:09.300 --> 00:50:11.770
of the population.
00:50:11.770 --> 00:50:20.820
From the mean value, if we go one standard
deviation up or one standard deviation down.
00:50:20.820 --> 00:50:33.690
Say, this the mean value; from mean value
if you go one standard deviation up one or
00:50:33.690 --> 00:50:46.349
one standard deviation down, then it is actually
covering, this is the mean value point, mid
00:50:46.349 --> 00:50:53.570
value, from that we are going plus one SD
or minus one SD, it is actually covering,
00:50:53.570 --> 00:50:58.619
what percentage of population it is actually
covering?
00:50:58.619 --> 00:51:01.380
68 percent of the population.
00:51:01.380 --> 00:51:10.260
This is observed for any data set which is
following normal distribution.
00:51:10.260 --> 00:51:21.470
Now, mean plus or minus one SD, it will cover
68 percent of the population.
00:51:21.470 --> 00:51:38.270
Now, from the mean value if we go two SD,
from here if we go two SD, this side up or
00:51:38.270 --> 00:51:51.520
two SD down, then two SD, here it is written
1.96 SD.
00:51:51.520 --> 00:51:53.740
So, the mean plus, value it is almost 2.
00:51:53.740 --> 00:51:58.339
So, mean plus 1.96 SD covers 95 percent of
the population.
00:51:58.339 --> 00:52:05.970
So, if we know the mean value, 160 centimeter,
from that one step, first case, mean value
00:52:05.970 --> 00:52:07.700
plus minus one SD.
00:52:07.700 --> 00:52:09.609
One into standard deviation three.
00:52:09.609 --> 00:52:16.630
So, 160 centimeter plus or minus one into
standard deviation value 3, this actually
00:52:16.630 --> 00:52:22.230
covers 68 percent of the population.
00:52:22.230 --> 00:52:30.900
From the mean value 160 centimeter, if we
go one time standard deviation value is 3,
00:52:30.900 --> 00:52:32.400
one into SD value 3.
00:52:32.400 --> 00:52:41.270
Actually that, within that range means 160
centimeter plus 3, means 163 centimeter to
00:52:41.270 --> 00:52:50.640
160 minus 3, 157 centimeter, within this range
68 percent of the population exist.
00:52:50.640 --> 00:52:59.490
Similarly, from the mean value 160 centimeter
if we go two into SD, SD value is 3, it is
00:52:59.490 --> 00:53:06.099
actually giving , it is actually covering
95 percent of the population, to which the
00:53:06.099 --> 00:53:10.530
actual value is 1.96, anyway I am putting
1.96.
00:53:10.530 --> 00:53:15.880
So, mean plus or minus 1.96 SD, SD value is
3 here in this case.
00:53:15.880 --> 00:53:19.560
Example, we have mentioned SD value is 3.
00:53:19.560 --> 00:53:29.829
So, mean value plus or minus 1.96 SD, within
that range how many, 95 percent of the population
00:53:29.829 --> 00:53:31.089
is covered.
00:53:31.089 --> 00:53:43.000
So, 160 minus 6, 154 centimeter to 160, plus
6, 166 centimeter, we can write in the opposite
00:53:43.000 --> 00:53:44.000
way.
00:53:44.000 --> 00:53:52.990
So, 157 to 163 centimeter, within this range
we are covering 68 percent, 154 to 166 centimeter,
00:53:52.990 --> 00:53:58.890
within this range, here we can cover 95 percent
of the population.
00:53:58.890 --> 00:54:13.550
So, from the mean value, if we go two standard
deviation, from this mean value, if we go
00:54:13.550 --> 00:54:22.089
2 two into standard deviation value then actually
we can cover 90 percent, this actually covers
00:54:22.089 --> 00:54:25.060
95 percent of the population.
00:54:25.060 --> 00:54:30.170
So, this is the range.
00:54:30.170 --> 00:54:40.030
The same thing is also shown here.
00:54:40.030 --> 00:54:44.230
So, from that mean value, this is the midpoint,
mean value.
00:54:44.230 --> 00:54:50.099
From the mean value if we go one SD upward
or one SD downwards.
00:54:50.099 --> 00:54:55.921
So, here we get that example, that if 160
centimeter is the mean value, from that if
00:54:55.921 --> 00:55:05.740
we add 3 centimeter positive or if we subtract
3 centimeter, within that range, 60, within
00:55:05.740 --> 00:55:10.349
that range, means, this doted portion, it
covers 68 percent.
00:55:10.349 --> 00:55:12.359
Similarly, from the mean value, that mid line.
00:55:12.359 --> 00:55:22.170
if we go almost two SD or 1.96 times of standard
deviation, then from this point to this point,
00:55:22.170 --> 00:55:30.609
it actually covers 95 percent of the population
and mean plus minus two into standard deviation,
00:55:30.609 --> 00:55:34.770
it actually covers 99 percent of the population.
00:55:34.770 --> 00:55:48.450
So, in this way we understand that if we can,
for a data set or whether that is anthropometric
00:55:48.450 --> 00:55:53.339
data or biomechanical data, from that data,
if we can calculate the mean value and we
00:55:53.339 --> 00:55:59.790
have the standard deviation value with us,
then we understand, from that mean either
00:55:59.790 --> 00:56:06.109
we will go within some range, positive direction
or negative direction, one SD up or one SD
00:56:06.109 --> 00:56:14.040
down, it will cover 68 percent mean value,
1.96 SD positive side, negative side, within
00:56:14.040 --> 00:56:23.520
this range it covers 95 percent of the population,
mean plus 2.58 SD, then it actually covers
00:56:23.520 --> 00:56:30.540
99 percent of the population.
00:56:30.540 --> 00:56:38.690
Now, one important thing, generally for the
design purpose, we mention that we want to
00:56:38.690 --> 00:56:45.319
use 5th percentile, 50th percentile, 95th
percentile anthropometric data, Why?
00:56:45.319 --> 00:56:53.130
From this graph, as we are discussing, if
we extend that, then we understand, mean plus
00:56:53.130 --> 00:56:59.690
minus one standard deviation actually covers
68 percent population, but what will you get,
00:56:59.690 --> 00:57:06.650
actually we are accommodating mean plus one
SD, if we calculate that actually give us
00:57:06.650 --> 00:57:14.619
the 84 percentile; mean minus one into standard
deviation value whatever value we will get
00:57:14.619 --> 00:57:17.450
that is actually 16 percentile data.
00:57:17.450 --> 00:57:26.840
So, it from 16th percentile to 84 percentile
actually, we are covering 68 percent of the
00:57:26.840 --> 00:57:28.670
population.
00:57:28.670 --> 00:57:39.020
Similarly, from mean value, if we go 1.64
SD in positive direction reaction or negative
00:57:39.020 --> 00:57:49.099
direction, it is actually covering 90 percent
of the population, mean, we are accommodating
00:57:49.099 --> 00:57:51.569
5th percentile to 95th percentile.
00:57:51.569 --> 00:57:58.760
So, in that way, you can mention, say, we
are mentioning this is covering 95 percent
00:57:58.760 --> 00:58:02.800
,means, remaining portion, this side remaining
portion is 2.5th percent, 5 percent.
00:58:02.800 --> 00:58:08.339
This side is also remaining portion is 2.5
percent.
00:58:08.339 --> 00:58:16.859
Similarly, if we want to cover mean plus 1.64
SD, it covers 90 percent of the population.
00:58:16.859 --> 00:58:24.000
If it covers 95 percent population, it means
5 percent population is below that range and
00:58:24.000 --> 00:58:30.770
5 percent population is above that range,
only it is covering mid 90 percent, means,
00:58:30.770 --> 00:58:39.160
this is the mean value ,say, this point is
160 centimeter, from that if we go 1.64 times
00:58:39.160 --> 00:58:46.020
SD value, then wherever we are reaching that
point is actually 95th percentile.
00:58:46.020 --> 00:58:51.380
So, you can easily calculate, how to calculate,
in other way, how to calculate 95th percentile
00:58:51.380 --> 00:58:57.440
data, if the data set follows a normal distribution,
then with the mean value, if we add 1.64 times
00:58:57.440 --> 00:59:05.990
SD then we are getting 95th percentile value,
mean value minus 1.64 SD then we are getting
00:59:05.990 --> 00:59:08.250
5th percentile value.
00:59:08.250 --> 00:59:16.290
So, within this range mean plus minus 1.64
SD actually, we are covering, starting from
00:59:16.290 --> 00:59:22.859
5th percentile and ending at 95th percentile.
00:59:22.859 --> 00:59:28.410
In between these two percentiles, 90 percent
of the population is covered.
00:59:28.410 --> 00:59:33.140
Similarly, from mean value, if with the mean
value, if we add one times SD, actually we
00:59:33.140 --> 00:59:34.540
are calculating 84th percentile data.
00:59:34.540 --> 00:59:40.380
Similarly, with mean value if we subtract
one SD, then actually we are calculating 16th
00:59:40.380 --> 00:59:41.380
percentile data.
00:59:41.380 --> 00:59:47.450
So, from 16th percentile to 84th percentile,
the range, which range we are covering?
00:59:47.450 --> 00:59:51.940
We are covering 68 percent of the population.
00:59:51.940 --> 01:00:06.510
So, in that way, in mean plus 1.96 SD, it
covers 95 percent of the population.
01:00:06.510 --> 01:00:21.240
Mean plus 1.96 SD actually, gives us the value
of 97.5th percentile data, mean minus 1.96
01:00:21.240 --> 01:00:26.340
SD give us the value of 2.5th percentile.
01:00:26.340 --> 01:00:35.480
So, starting from 2.5th percentile to 97.5th
percentile, we are accommodating 90 percent
01:00:35.480 --> 01:00:38.680
of the population.
01:00:38.680 --> 01:00:47.590
Next one; with the mean value if we add or
subtract 2.58 SD then actually we can cover
01:00:47.590 --> 01:00:52.690
99 percent of the population or data set.
01:00:52.690 --> 01:01:00.670
It means, it actually, starting point is 0.5th
percentile and end point is 99.5th percentile.
01:01:00.670 --> 01:01:07.480
Now, the question, why do we use 5th, 50th
or 95th percentile?
01:01:07.480 --> 01:01:14.010
So, generally 50th percentile is called average
dimension, we will discuss in detail.
01:01:14.010 --> 01:01:23.109
So, starting from 5th and ending at 95th,
if we understand this graph, then we, from
01:01:23.109 --> 01:01:32.569
that, we can visualize this, that mean in
plus minus one SD covers 68 percent.
01:01:32.569 --> 01:01:38.960
If we increase it to mean plus minus 1.64
SD, we are covering actually 90; starting
01:01:38.960 --> 01:01:42.440
from 68, now we are covering 90 percent.
01:01:42.440 --> 01:01:49.940
So, if we increase the SD value from 1, from
1 to 1.64 actually we are covering another
01:01:49.940 --> 01:01:57.440
22 percent of the population, if you we consider
all only mean one SD we can cover 68 percent
01:01:57.440 --> 01:02:04.520
of the population, but if we consider mean
plus minus 1.64 SD then we are covering 90
01:02:04.520 --> 01:02:05.990
percent of the population.
01:02:05.990 --> 01:02:13.280
So, what is the extra percentage we are covering
by using this 0.64 SD more, we are actually
01:02:13.280 --> 01:02:16.180
covering 22 percent extra.
01:02:16.180 --> 01:02:21.740
Similarly, if we go for 1.96 SD then we can
cover 95 percent.
01:02:21.740 --> 01:02:29.330
So, what is the increment from the 90 percent,
now it is 95 percent earlier it is 90 percent,
01:02:29.330 --> 01:02:32.230
now it is 95 percent.
01:02:32.230 --> 01:02:36.890
So, only 5 percent extra you can accommodate.
01:02:36.890 --> 01:02:43.270
But, this indicates that while we are increasing
the SD value from 1 to 1.64 it is accommodating
01:02:43.270 --> 01:02:50.109
a good amount, it is increasing the accommodation
of good amount of population, that is 22 percent
01:02:50.109 --> 01:03:01.040
extra,, but from 1.64 SD if we increase almost
1.64 to almost 1.96 then only we are increasing
01:03:01.040 --> 01:03:04.240
the accommodation of 5 percent extra people.
01:03:04.240 --> 01:03:09.890
So, we should not go for this, we should go
for only this one, which is better because,
01:03:09.890 --> 01:03:13.430
as much as this standard deviation value,
what is the standard deviation value means,
01:03:13.430 --> 01:03:20.290
actually we can explain it in some other way
,say, for example, if we are designing this
01:03:20.290 --> 01:03:21.720
chair, in this chair.
01:03:21.720 --> 01:03:28.400
So, if this is the mean height of the chair,
for mean height of the chair, seat-pan height
01:03:28.400 --> 01:04:04.369
is 40 centimeter, this 40 centimeter seat-pan
height or you can draw it.
01:04:04.369 --> 01:04:17.849
So, this is a chair.
01:04:17.849 --> 01:04:18.950
This height is 40 centimeter.
01:04:18.950 --> 01:04:27.410
So, we are assuming that leg height for that
chair is, mean value is 40 centimeter.
01:04:27.410 --> 01:04:35.570
So, how we have decided that what should be
the leg height of that chair.
01:04:35.570 --> 01:04:37.849
So, for that purpose, we have collected the
anthropometric data.
01:04:37.849 --> 01:04:42.240
From that anthropometric data set, we have
calculated the the mean value or 50th percentile
01:04:42.240 --> 01:04:43.240
value.
01:04:43.240 --> 01:04:46.619
So, mean value the 50th percentile is coming
to 40 centimeter.
01:04:46.619 --> 01:04:56.380
Now, for that particular 40 centimeter, this
is leg height value for the chair, we need
01:04:56.380 --> 01:04:59.610
design this one, what should be the value?
01:04:59.610 --> 01:05:02.910
For this purpose, what is the corresponding
anthropometric value, what is the human body
01:05:02.910 --> 01:05:04.780
dimension for that? that is, we call popliteal
height.
01:05:04.780 --> 01:05:09.880
In general term, we can mention is, okay,
leg height, leg height of human.
01:05:09.880 --> 01:05:17.840
So, one human being is sitting there, we need
to consider his/her leg height, this is the
01:05:17.840 --> 01:05:23.040
leg.
01:05:23.040 --> 01:05:44.690
If you imagine this is the human being, when
that human being is sitting, this is the dimension
01:05:44.690 --> 01:05:50.580
we need to consider, exact anthropometric
variable, we call it popliteal height, that
01:05:50.580 --> 01:05:56.430
height, from below the thigh up to the bottom
surface of the sole.
01:05:56.430 --> 01:05:57.430
So, that is the popliteal height.
01:05:57.430 --> 01:06:04.750
So, popliteal height, we measure the popliteal
height of a big sample and we are getting,
01:06:04.750 --> 01:06:07.450
calculating the mean value is 40 centimeter.
01:06:07.450 --> 01:06:15.480
So, accordingly if we design this chair as
per the 40 centimeter popliteal height then
01:06:15.480 --> 01:06:23.520
50th, 50th percentile person can sit comfortably
because it is as per his body dimension.
01:06:23.520 --> 01:06:34.530
Now from that height, if we, now from side
view, so this is the chair, initially it is
01:06:34.530 --> 01:06:38.020
at the mean value, that is 40 centimeter.
01:06:38.020 --> 01:06:46.690
Now, if we increase this height, the standard
deviation value of that anthropometric variable
01:06:46.690 --> 01:06:52.780
is two, that anthropometric variable means
popliteal height or we can mention leg height.
01:06:52.780 --> 01:06:57.579
So, popliteal and the standard deviation value
is two.
01:06:57.579 --> 01:07:05.690
So, with mean plus from that if we go one
SD, one SD means 2 centimeter, 40 centimeter,
01:07:05.690 --> 01:07:14.940
if we go 40 centimeter to 42 centimeter or
you can come to 38 centimeter, from that mean
01:07:14.940 --> 01:07:21.369
value, if you go 2 centimeter up or 2 centimeter
down, means1SD, one times SD, then we can
01:07:21.369 --> 01:07:25.349
accommodate 68 percent of the people.
01:07:25.349 --> 01:07:40.550
But if we go mean plus 1.96 SD up and down,
1.96 SD means almost 2 SD mean plus one point.
01:07:40.550 --> 01:07:49.190
So, if you consider 1.64 SD, now from mean
this is the mean value, for mean value if
01:07:49.190 --> 01:07:56.130
we go 1.64 SD then we can cover.
01:07:56.130 --> 01:08:00.660
So, this is 1.64 SD, then we can cover, from
this is the mean value, from that mean value
01:08:00.660 --> 01:08:09.130
we are increasing 1.64 SD up, 1.64 SD down
then within this range, from this point to
01:08:09.130 --> 01:08:12.210
this point, how much percentage we are covering?
01:08:12.210 --> 01:08:16.199
We are covering 90 percent of the population.
01:08:16.199 --> 01:08:24.199
So, initially we were using mid mean, from
mean value 1 SD up, 1 SD down we are covering
01:08:24.199 --> 01:08:25.230
68 percent.
01:08:25.230 --> 01:08:32.969
Now from the mean value, we are going 1.64SD
up, 1.64 SD down, within that range, we are
01:08:32.969 --> 01:08:33.969
covering 90 percent.
01:08:33.969 --> 01:08:40.339
So, actually we are increasing the accommodation
of 22 percent more people.
01:08:40.339 --> 01:08:46.870
So, for that purpose, we you should go for,
we should always try to accommodate 90 percent.
01:08:46.870 --> 01:08:53.150
We are accommodating 90 percent of the population,
means, it is starting from 5th percentile
01:08:53.150 --> 01:09:02.140
to and ending at 95th percentile; 5th to 95th
percentile people with this popliteal height
01:09:02.140 --> 01:09:08.609
or lower leg height, starting from 5th percentile
to 95th percentile, means, in between this,
01:09:08.609 --> 01:09:18.310
90 percent of the population, they will be
able use this seat, if the height adjustability
01:09:18.310 --> 01:09:31.690
of the seat we adjust like 1.64 SD up and
down.
01:09:31.690 --> 01:09:42.949
So, from mean value, so this is beneficial
for us.
01:09:42.949 --> 01:09:52.980
From mean, if you go 1 SD then only we can
cover 68 percent, but from mean if we increase
01:09:52.980 --> 01:09:55.880
or decrease 1.64 SD then we can cover 90 percent.
01:09:55.880 --> 01:10:00.000
So, extra 22 percent can be covered or can
be accommodated but after that again, if we
01:10:00.000 --> 01:10:05.960
increase the SD value, only we can increase
accommodation of 5 percent, from 1.64 SD,
01:10:05.960 --> 01:10:11.230
if we increase to 2.58 SD then we are increasing
only 9 percent.
01:10:11.230 --> 01:10:16.900
For that purpose, we do not go, because as
much as standard deviation value ,means, as
01:10:16.900 --> 01:10:23.290
much as adjustable feature, we will add then
the facility will become fragile, it will
01:10:23.290 --> 01:10:28.460
be broken easily, for that purpose we should
not go, we should not go for increasing the
01:10:28.460 --> 01:10:30.949
SD value as much as possible.
01:10:30.949 --> 01:10:35.990
If we increase, you can increase it, mean
plus 2SD, SD 3 SD you can increase, but with
01:10:35.990 --> 01:10:43.210
that increment, the number of people or the
percentage of the population being accommodate,
01:10:43.210 --> 01:10:51.000
that is very less, for that purpose, we will
concentrate on this area, this area ,means,
01:10:51.000 --> 01:10:52.000
mean plus 1.64 SD.
01:10:52.000 --> 01:10:57.739
If you use, then you can cover 90 percent
of the population, means, we are considering
01:10:57.739 --> 01:11:02.350
5th percentile to 95th percentile of the anthropometric
data.
01:11:02.350 --> 01:11:07.390
If we use the 5th percentile to 95th percentile
anthropometric data, then we are covering
01:11:07.390 --> 01:11:10.390
actually 90 percent population.
01:11:10.390 --> 01:11:18.400
But for that purpose, how much SD you are
using, you are using only 1.64 times SD.
01:11:18.400 --> 01:11:24.070
So, this is very good compromise because we
can also increase the standard deviation,
01:11:24.070 --> 01:11:28.670
but as much as standard deviation value will
increase ,mean, we will increase the more
01:11:28.670 --> 01:11:40.300
adjustable feature, that system on that facility
will be fragile and it will be easy to break
01:11:40.300 --> 01:11:41.300
down.
01:11:41.300 --> 01:11:49.970
So, this is the reason for which, to accommodate
more number of people we go for up to accommodating
01:11:49.970 --> 01:11:54.880
90 percent population, it is 5th percentile
to 95th percentile,, but we should not go
01:11:54.880 --> 01:11:59.690
for accommodating 0.5th percentile to 99.5th
percentile, 99.5th percentile because in that
01:11:59.690 --> 01:12:05.420
case, in that case, we have to use 2.58 times
SD, while we use more SD value or mean, we
01:12:05.420 --> 01:12:19.600
are making the adjustable feature more, it
is becoming much more fragile and only very
01:12:19.600 --> 01:12:24.810
limited number of people are being accommodated
with the increase of standard deviation value.
01:12:24.810 --> 01:12:34.180
So, for this reason, we use, keep the 95th
percentile data to accommodate the 90 percent
01:12:34.180 --> 01:12:41.140
population, where only we use 1.64 times SD.
01:12:41.140 --> 01:12:48.900
Now, already we discussed this one, that for,
if we know the mean value and standard deviation
01:12:48.900 --> 01:12:54.340
value for a data set which is following normal
distribution, then we can calculate various
01:12:54.340 --> 01:12:59.610
percentile values, first percentile, second
percentile or 5th percentile or 50th percentile
01:12:59.610 --> 01:13:01.370
as per our requirement.
01:13:01.370 --> 01:13:08.920
So, here these are the ‘Z’ is the constant
value, here is also the constant value, if
01:13:08.920 --> 01:13:15.280
you want to calculate ,say, for example, 5th
percentile or 95th percentile value, then
01:13:15.280 --> 01:13:19.820
how to calculate?
01:13:19.820 --> 01:13:22.810
This is the constant value, for a specific
percentile if you want to calculate then we
01:13:22.810 --> 01:13:28.460
have to go for mean value plus or minus standard
deviation value.
01:13:28.460 --> 01:13:35.500
See if want to consider 5th percentile, calculate
5th percentile value then what we will do?
01:13:35.500 --> 01:13:41.270
from mean obviously, mean means 50th percentile,
mean value indicates 50th percentile value
01:13:41.270 --> 01:13:42.800
in case of normal distribution.
01:13:42.800 --> 01:13:48.810
So, if the 50th percentile value, obviously,
5th percentile value will be less than that,
01:13:48.810 --> 01:13:54.200
for that purpose we have to go for minus,
this value is the minus.
01:13:54.200 --> 01:13:57.400
So, mean minus 1.64 times.
01:13:57.400 --> 01:14:03.710
So, this minus 1.64 times, if you have to
calculate 5th percentile, then with standard
01:14:03.710 --> 01:14:13.010
deviation, then you have to multiply minus
1.64 SD, mean minus 1.64 times SD, then you
01:14:13.010 --> 01:14:15.500
we are getting 5th percentile value.
01:14:15.500 --> 01:14:20.900
Similarly, if you want to calculate the 95th
percentile value, then how we have to proceed,
01:14:20.900 --> 01:14:26.010
mean value or 50th percentile value, with
that 50th percentile value we have to add
01:14:26.010 --> 01:14:31.840
1.64 times SD then we will be able to calculate
95th percentile value.
01:14:31.840 --> 01:14:38.530
Similarly, say if you want to calculate 25th
percentile value, in that case what you have
01:14:38.530 --> 01:14:47.030
to do, to calculate the 25th percentile data,
mean minus 0.67 SD, then you will be able
01:14:47.030 --> 01:14:49.719
to calculate 25th percentile value.
01:14:49.719 --> 01:14:56.560
Similarly, if you want to calculate 75th percentile
value, for that purpose we have to calculate
01:14:56.560 --> 01:15:04.270
mean plus 0.67 into standard deviation, then
we get the 75th percentile value.
01:15:04.270 --> 01:15:10.530
So, from this type of standard values of ‘Z’
we can calculate various percentile, ‘Z’
01:15:10.530 --> 01:15:17.140
value or of the constant value, with that
constant, we have to multiply or calculate
01:15:17.140 --> 01:15:29.500
a standard deviation value to calculate various
percentile data.
01:15:29.500 --> 01:15:36.060
Now, body planes.
01:15:36.060 --> 01:15:42.449
Now after, so far what we discussed.
01:15:42.449 --> 01:15:46.690
So, far we discussed, if we recall.
01:15:46.690 --> 01:15:54.210
So, few basic statistical parameters like
percentage, then percentile, gradually moved
01:15:54.210 --> 01:16:00.360
to various types of central tendency mean,
median, mode.
01:16:00.360 --> 01:16:09.080
Mean is the 50th percentile value in case
of normal distribution because in case of
01:16:09.080 --> 01:16:12.420
normal distribution, mean, median and mode
value coincide.
01:16:12.420 --> 01:16:21.710
Then we again discussed about the standard
deviation value, how it is calculated and
01:16:21.710 --> 01:16:27.020
what is the meaning of standard deviation,
then normal distribution, then we mentioned,
01:16:27.020 --> 01:16:34.410
we also discussed, how if you we know the
mean value and standard deviation value for
01:16:34.410 --> 01:16:37.270
a particular data set which is following normal
distribution, then we can calculate various
01:16:37.270 --> 01:16:44.050
percentile value, at the same we understand
that how a specific range, either 68 percent
01:16:44.050 --> 01:16:51.630
or 90 percent or 95 percent population, within
which range it is actually being covered.
01:16:51.630 --> 01:16:55.880
So, that is also shown here, that if we understand,
know the mean value or standard deviation
01:16:55.880 --> 01:17:05.120
value, then we can calculate and we can accommodate
certain percentage of the population.
01:17:05.120 --> 01:17:10.260
Then this is the same calculation of percentile,
now after understanding this basic statistics,
01:17:10.260 --> 01:17:16.390
now we are moving towards the, our main topic;
anthropometric and biomechanical data.
01:17:16.390 --> 01:17:23.820
So, for that purpose initially, you should
know little bit about the human anatomy, body
01:17:23.820 --> 01:17:26.680
planes and various landmarks.
01:17:26.680 --> 01:17:36.400
So, basic knowledge of human body, this body
planes are important, here you can see, we
01:17:36.400 --> 01:17:38.580
can describe human body through various planes.
01:17:38.580 --> 01:17:47.480
So, if you look at this plane, this one, there
are different directions, here it is mentioned
01:17:47.480 --> 01:17:49.520
X, Y, Z direction.
01:17:49.520 --> 01:17:50.520
deviation.
01:17:50.520 --> 01:17:58.780
So, if forward direction is X and upward direction
is Z, then this plane is called XZ, XZ plane.
01:17:58.780 --> 01:18:05.420
Forward direction in front, if we mention,
forward direction is X , upward direction
01:18:05.420 --> 01:18:13.730
is Z, then this plane is called XZ, this plane
is called Sagittal plane.
01:18:13.730 --> 01:18:16.790
So, what this sagittal plane is doing?
01:18:16.790 --> 01:18:23.110
Sagittal plane is actually the plane which
is dividing our body in left and right half.
01:18:23.110 --> 01:18:29.380
So, this is the plane in front of your body,
if we assume that forward direction is X-axis,
01:18:29.380 --> 01:18:35.260
upward direction is Z-axis then this XZ plane,
it is dividing our body into two halves, this
01:18:35.260 --> 01:18:39.410
is called sagittal plane.
01:18:39.410 --> 01:18:50.600
Another is, if you look at this plane, this
plane YZ plane, if we mention that Y-axis
01:18:50.600 --> 01:18:54.510
is sidewise, Z-axis is upward, Y-axis sidewise
and Z-axis upward.
01:18:54.510 --> 01:19:01.660
So, this plane is actually YZ plane, YZ plane
is called Coronal plane.
01:19:01.660 --> 01:19:09.510
This Coronal plane is dividing our body in
frontal, forward and backward; frontal and
01:19:09.510 --> 01:19:12.890
backward or ventral and dorsal halves.
01:19:12.890 --> 01:19:16.840
So, this plane is called Coronal plane.
01:19:16.840 --> 01:19:25.550
So, one plane is sagittal plane that is dividing
our body into two halves, left and right.
01:19:25.550 --> 01:19:32.380
Similarly, this plane YZ plane dividing our
body frontal and backward portion, back portion
01:19:32.380 --> 01:19:35.410
mean ventral or dorsal half that is called
Coronal plane.
01:19:35.410 --> 01:19:45.429
Similarly, there is another plane, that is
if X-axis is forward and Y-axis is sidewise,
01:19:45.429 --> 01:19:51.179
X forward Y sidewise then, this plane, this
plane is called Transverse plane.
01:19:51.179 --> 01:19:58.449
So, this is dividing our body into two halves
up and down.
01:19:58.449 --> 01:20:05.360
So, these planes are very important because
while during our anthropometric and bio mechanical
01:20:05.360 --> 01:20:10.940
discussion or biomechanical study while we
are discussing human body parts or its movement
01:20:10.940 --> 01:20:18.429
then we have to explain that, say, for example,
you are describing hand movement, then we
01:20:18.429 --> 01:20:22.830
have to mention in which direction X, Y, Z
that coordinate system, at the same time in
01:20:22.830 --> 01:20:26.489
which body plane, that particular body parts
is moving.
01:20:26.489 --> 01:20:37.260
So, defining the posture, defining the human
body motion, these planes are very helpful.
01:20:37.260 --> 01:20:44.730
Now, similarly if you look at, you should
also know, for the students or students it
01:20:44.730 --> 01:20:49.290
is important to understand basic anatomy of
the human body, the skeleton structure, number
01:20:49.290 --> 01:20:55.020
of bones, how these bones are arranged in
the human body, all these information is very
01:20:55.020 --> 01:20:57.960
much important for digital human modelling
because in digital human model, what we do,
01:20:57.960 --> 01:21:06.770
as we have mentioned earlier also that is
actually human body representation, digital
01:21:06.770 --> 01:21:10.780
human modeling is CAD representation of human
body.
01:21:10.780 --> 01:21:17.699
So, if we know basic anatomy and the structure
of human body then it will be easy for us
01:21:17.699 --> 01:21:19.270
to understand digital human model creation
and its use for the different types of ergonomic
01:21:19.270 --> 01:21:24.430
evolution
So, from any anatomy book we can go through
01:21:24.430 --> 01:21:30.430
and we can understand that how those bones
are attached with one to 1 another, different
01:21:30.430 --> 01:21:43.100
types of joints are there and the same time
landmarks, there is another word for landmark.
01:21:43.100 --> 01:21:48.460
So, there are some specific points on the
body which we can clearly identify from outside,
01:21:48.460 --> 01:21:54.580
so that if you want to measure human body
dimension ,say, if I want to measure hand
01:21:54.580 --> 01:21:56.750
dimension, then from this point to that point.
01:21:56.750 --> 01:22:02.880
So, there are some specific points on the
body which should be identified, which can
01:22:02.880 --> 01:22:14.000
be easily identified by some other person
also and from those ,say, if you mention that
01:22:14.000 --> 01:22:23.180
lower arm length, from where to start and
where to end, how do we define, for that purpose,
01:22:23.180 --> 01:22:30.500
specific bony marks, on the specific position
of the bony portion those we have to identify,
01:22:30.500 --> 01:22:33.960
protrusion or any other foldings, we have
to identify, from that we will measure the
01:22:33.960 --> 01:22:34.960
body dimension.
01:22:34.960 --> 01:22:35.960
So, different landmarks are for example, one
is Acromion.
01:22:35.960 --> 01:22:41.840
So, this, we have to know that this is the
Acromion position.
01:22:41.840 --> 01:22:48.170
Then those, if we know those points then it
will be easier for us to measure human body
01:22:48.170 --> 01:22:50.030
dimension.
01:22:50.030 --> 01:22:56.820
Now, if you look at our all overall skeleton
structure.
01:22:56.820 --> 01:23:04.480
So, this the vertebral column where our all
skull is attached, this vertebral column.
01:23:04.480 --> 01:23:12.550
See in vertebral column or our back bone is
made up of many bones, if we look at the cervical
01:23:12.550 --> 01:23:15.720
portion or neck portion, how many bones are
there?
01:23:15.720 --> 01:23:19.820
There are total seven bones, seven movable
bones, with these seven movable bones, we
01:23:19.820 --> 01:23:21.050
can move our neck.
01:23:21.050 --> 01:23:27.410
Then next portion is the thoracic portion
or chest portion, there are total 12 movable
01:23:27.410 --> 01:23:28.880
bones are there.
01:23:28.880 --> 01:23:35.750
Next in thoracic portion, in thoracic portion,
chest portion there are total 12 bones.
01:23:35.750 --> 01:23:39.190
Next portion is called lumbar portion.
01:23:39.190 --> 01:23:46.500
So, first neck portion, then thoracic, then
lower back portion, this portion is called
01:23:46.500 --> 01:23:47.949
actually lumbar area.
01:23:47.949 --> 01:23:53.880
This lumbar area, here it is representing
like this, in lumbar area how many bones are
01:23:53.880 --> 01:23:54.880
there?
01:23:54.880 --> 01:23:57.310
five movable bones are there.
01:23:57.310 --> 01:24:05.909
Lumbar area is very important for our discussion
because our forward bending or sidewise bending
01:24:05.909 --> 01:24:14.239
or rotating, even bending backward all these
actually happening with the help of this lumbar
01:24:14.239 --> 01:24:16.840
bones, these are very flexible.
01:24:16.840 --> 01:24:23.480
So, for, while we are talking about sitting
ergonomics, workstation ergonomics, then lumbar
01:24:23.480 --> 01:24:29.960
motion is very important because this flexible
portion actually allows us, although all other
01:24:29.960 --> 01:24:34.150
movable bones are also movable, thoracic or
cervical, but our bend, forward bending or
01:24:34.150 --> 01:24:36.330
leaning or we are leaning backward.
01:24:36.330 --> 01:24:43.130
So, these actually happens due to this thoracic
and lumbar bones and this, in lumbar area
01:24:43.130 --> 01:24:50.880
during heavy load carriage or load lifting
or bending, there is compression, we will
01:24:50.880 --> 01:24:51.880
discuss further.
01:24:51.880 --> 01:24:52.890
Next is the sacral, next portion is the sacral
portion.
01:24:52.890 --> 01:24:55.650
This is the sacral portion.
01:24:55.650 --> 01:24:58.969
So, what is sacral sector portion?
01:24:58.969 --> 01:25:06.260
In sacral portion there are total five immovable
bones.
01:25:06.260 --> 01:25:18.120
So, if we see the backside view, then we can
see, these are the sacral portion.
01:25:18.120 --> 01:25:24.430
So, actually there are five vertebrae, those
five vertebrae fused together and those are
01:25:24.430 --> 01:25:25.430
immovable.
01:25:25.430 --> 01:25:30.100
Similarly, coccygeal portion, what we call,
tail portion, in coccygeal portion there are
01:25:30.100 --> 01:25:32.440
four immovable bones, those are also fused.
01:25:32.440 --> 01:25:44.390
So, in this way, total there are 7 and 12,
19 and 10, 5 and 4, total 33 bones are there,
01:25:44.390 --> 01:25:47.580
as this two portion immovable and those bones
are fused.
01:25:47.580 --> 01:26:00.100
So, actually this is coming to 24, 24 movable
bones and these 2 are, these five fused to
01:26:00.100 --> 01:26:06.130
only making only one structure and these four
immovable bones are fused and they are also
01:26:06.130 --> 01:26:07.150
making one structure.
01:26:07.150 --> 01:26:12.850
So, there are total, these are the seven,
twelve, five, five and four number of bones.
01:26:12.850 --> 01:26:24.030
Now, in between these, if you look at this
vertebrae, in between two vertebrae, mean
01:26:24.030 --> 01:26:34.010
two bony structures, there is some soft tissue
material that is called inter-vertebral disc.
01:26:34.010 --> 01:26:44.219
24 movable bony vertebrae are separated by
23 deformable hydraulic pads of fibro-cartilage
01:26:44.219 --> 01:26:47.350
known as inter-vertebral discs.
01:26:47.350 --> 01:26:54.199
So, in between two vertebrae, this portion
is a actually inter-vertebral disc.
01:26:54.199 --> 01:27:07.990
So, during our bending that inter-vertebral
disc actually get compressed.
01:27:07.990 --> 01:27:17.010
Next, as we are discussing about anatomical
structure, this is from another source.
01:27:17.010 --> 01:27:19.570
So, various body parts, if we talk about hand.
01:27:19.570 --> 01:27:27.030
So, we, at least we have basic knowledge that
there are, how many bones are there are there.
01:27:27.030 --> 01:27:33.320
So, upper portion there is Femurous bone and
lower arm, there are two bones radial and
01:27:33.320 --> 01:27:34.320
ulnar.
01:27:34.320 --> 01:27:40.420
So, this type of and though how, what is the
joint type, what is the how that particular
01:27:40.420 --> 01:27:45.620
body joint moves, so to this basic knowledge
is important, otherwise it will not possible
01:27:45.620 --> 01:27:53.620
for us to use digital human modeling software,
creating human model and going for the various
01:27:53.620 --> 01:28:01.630
types of ergonomic evolution and while we,
later on, while in this course, while do we
01:28:01.630 --> 01:28:06.450
discuss about the, creating the digital human
model, then this one – skeletal linkage
01:28:06.450 --> 01:28:10.780
system, that how human body can be represented
with link segment, various body segments those
01:28:10.780 --> 01:28:14.290
are joined with this linked structure.
01:28:14.290 --> 01:28:16.130
So, this is linkage system.
01:28:16.130 --> 01:28:23.880
The same hand, upper arm, lower arm or from
how we can represent with linked segment,
01:28:23.880 --> 01:28:26.970
that is actually showing the skeleton linkage
system.
01:28:26.970 --> 01:28:34.690
This will be very much helpful, if you understand
this one clearly, then this will be helpful
01:28:34.690 --> 01:28:38.120
for our making digital human modeling.
01:28:38.120 --> 01:28:45.570
Now, continuation to our earlier slide earlier,
where we were discussing about inter-vertebral
01:28:45.570 --> 01:28:47.590
disc, in between two vertebrae there is inter-vertebral
disc.
01:28:47.590 --> 01:28:49.969
So, what happens in the inter-vertebral disc?
01:28:49.969 --> 01:28:58.290
There are this type of, this is a say, that
one vertebra and another vertebra, in between
01:28:58.290 --> 01:29:00.830
two vertebrae there are inter-vertebral disc.
01:29:00.830 --> 01:29:03.380
So, while we are bending forward?
01:29:03.380 --> 01:29:05.920
Then, what is happening, this side?
01:29:05.920 --> 01:29:10.600
One side it is getting compressed, another
side it is expanding.
01:29:10.600 --> 01:29:16.690
So, what happens, while there is compressive
force inter- vertebral disc consist of two
01:29:16.690 --> 01:29:25.400
parts, that is the, central part is called
the, center called as nucleus pulposus and
01:29:25.400 --> 01:29:30.110
surrounding part is called as annulus fibrosus.
01:29:30.110 --> 01:29:32.530
This regarding the inter-vertebral disc.
01:29:32.530 --> 01:29:40.370
Now, we are discussing about the force, due
to this movement of the vertebrae, while we
01:29:40.370 --> 01:29:45.940
are bending forward and backward say, if we
are moving forward then what is happening?
01:29:45.940 --> 01:29:53.159
Then this corner, while we are moving forward
or bending forward then here it is getting
01:29:53.159 --> 01:29:54.159
compressed.
01:29:54.159 --> 01:29:57.200
So, what type of compression is there, there
is, compressive force is there, while there
01:29:57.200 --> 01:30:01.890
is a compressive force, at the same time,
this side, there is shearing force.
01:30:01.890 --> 01:30:08.969
Similarly, here while we are bending backward
then here is the compressive force.
01:30:08.969 --> 01:30:16.940
So, due to all, various type of bending, twisting,
rotating body action in the inter-vertebral
01:30:16.940 --> 01:30:22.690
disc there is change in the pressure, sometimes
there is compressive force, sometimes there
01:30:22.690 --> 01:30:24.040
is increasing shearing force.
01:30:24.040 --> 01:30:35.980
Kapandji in 1974, he mentioned the disc resist
the compressive load and facets resists the
01:30:35.980 --> 01:30:36.980
inter-vertebral shearing force.
01:30:36.980 --> 01:30:45.890
So, this disc, actually help us in resisting
the compressive force while there is compression,
01:30:45.890 --> 01:30:51.719
at the same time, this type of facet joint
of the vertebrae, it helps in resisting the
01:30:51.719 --> 01:30:54.739
shearing force.
01:30:54.739 --> 01:31:14.980
Now, coming to our main topic, that is anthropometry
and use of anthropometric data, first we know,
01:31:14.980 --> 01:31:16.790
what is anthropometry?
01:31:16.790 --> 01:31:22.210
‘Anthropo’ means related to human and
‘metery’ means measurement.
01:31:22.210 --> 01:31:30.480
So, anthropometry actually indicates the measurement
of the human body.
01:31:30.480 --> 01:31:39.370
So, it is defined as measurement of human
body dimension in order to optimize the interface
01:31:39.370 --> 01:31:44.920
between man-machine and other manufactured
product.
01:31:44.920 --> 01:31:52.600
So, this measurement of human body is of two
types; one is static measurement and another
01:31:52.600 --> 01:31:54.390
is dynamic measurement.
01:31:54.390 --> 01:32:02.420
So, this is also mentioned as static anthropometry
and dynamic anthropometry.
01:32:02.420 --> 01:32:03.960
So, what is static anthropometry?
01:32:03.960 --> 01:32:07.960
Static anthropometry, while human body is
in the static posture, mean, either seated
01:32:07.960 --> 01:32:16.190
or in standing posture, means, there is no
movement while human body is in static posture,
01:32:16.190 --> 01:32:22.429
that time whatever body dimension, we are
measuring following standard measurement procedure
01:32:22.429 --> 01:32:29.530
with standard instrument that is called static
anthropometric measurement; example of static
01:32:29.530 --> 01:32:30.530
anthropometric measurement.
01:32:30.530 --> 01:32:31.530
Say, for example, hand length.
01:32:31.530 --> 01:32:39.739
So, what is the length of lower arm or upper
arm, what is the height of the head?
01:32:39.739 --> 01:32:43.810
So, all this measurement we can take while
human body is in static posture.
01:32:43.810 --> 01:32:50.719
Sitting height while some is seated on the
chair, then you can measure the height from
01:32:50.719 --> 01:32:52.190
the ground, up to the head.
01:32:52.190 --> 01:32:56.090
This the sitting height from the ground upto
the head height.
01:32:56.090 --> 01:32:57.929
So, that is sitting height.
01:32:57.929 --> 01:33:01.390
Similarly, while someone is standing, then
in the standing posture you can measure from
01:33:01.390 --> 01:33:03.140
the ground this is the acromion point.
01:33:03.140 --> 01:33:04.140
So, acromion height.
01:33:04.140 --> 01:33:11.360
In that way, in different static posture you
can measure human body dimension that may
01:33:11.360 --> 01:33:19.630
not be always linear, only length or breadth,
that may be even the circumferential dimension
01:33:19.630 --> 01:33:24.050
say, wrist circumference or hand width diameter.
01:33:24.050 --> 01:33:32.270
So, different types of linear or circumferential
diameter measurement you we can take.
01:33:32.270 --> 01:33:38.630
So, static measure, static or structural anthropometric
data or body dimension measured with body
01:33:38.630 --> 01:33:40.141
building standardized static posture.
01:33:40.141 --> 01:33:47.100
Static dimension refer to the actual sizes
of the body components and include simple
01:33:47.100 --> 01:33:50.949
length or linear and circumferential dimensions,
contours etcetera.
01:33:50.949 --> 01:33:56.250
These include height, widths, breadth, depth
and usually imply no direction.
01:33:56.250 --> 01:34:08.460
So, their direction is
not important.
01:34:08.460 --> 01:34:13.310
For example, foot length, now, how these anthropometric
measurements are taken?
01:34:13.310 --> 01:34:18.159
In earlier days physical measurements with
anthropometric kit, anthropometric rods, different
01:34:18.159 --> 01:34:21.610
types of calipers people use to take physical
measurement.
01:34:21.610 --> 01:34:26.230
There is also other technique, photography,
photographic technique, in photographic technique
01:34:26.230 --> 01:34:37.489
photo is taken from front view or side-view
and after that with scale we can or with chart
01:34:37.489 --> 01:34:39.500
paper we can calculate the body dimension.
01:34:39.500 --> 01:34:46.400
But, nowadays there is 3D body scanning technology,
with 3D body scanner what you can do?
01:34:46.400 --> 01:34:53.739
You can scan the whole human body and from
the scanned data, we can go for measure, ‘n’
01:34:53.739 --> 01:34:56.860
number of body dimension including linear
dimension or any other non-linear dimension
01:34:56.860 --> 01:34:57.860
we can measure.
01:34:57.860 --> 01:35:05.090
Even we can measure at every cross-section;
what is the area of the human body.
01:35:05.090 --> 01:35:16.070
So with the advancement of technology, with
3D body scanning now it is much more easier
01:35:16.070 --> 01:35:22.150
and to get more accurate data of human body
shape and sizes.
01:35:22.150 --> 01:35:29.880
Now, dynamic measures, dynamic or functional
measures are taken with the body while it
01:35:29.880 --> 01:35:30.880
is in motion.
01:35:30.880 --> 01:35:38.190
So, in this case, the body was in static posture,
in this case body is in dynamic posture.
01:35:38.190 --> 01:35:44.440
So, while body is in movement, then whatever
body dimensions are or dimensions are measured
01:35:44.440 --> 01:35:50.910
that is called dynamic measurements and usually
more complex and difficult to measure.
01:35:50.910 --> 01:35:57.550
Dynamic dimensions refers to ability of the
body to perform certain tasks, within certain
01:35:57.550 --> 01:36:03.710
distances spaces or enclosures and include
the description of measurement of human mobility,
01:36:03.710 --> 01:36:05.350
agility, flexibility.
01:36:05.350 --> 01:36:12.460
So one example, if you we want to measure
the range of motion of this hand.
01:36:12.460 --> 01:36:16.340
So, this hand in this particular example,
this called flexion.
01:36:16.340 --> 01:36:17.340
friction.
01:36:17.340 --> 01:36:20.929
So, flexion friction or this is opposite direction
that is extension.
01:36:20.929 --> 01:36:25.409
So, we can measure the complete range of motion.
01:36:25.409 --> 01:36:27.360
So, this is not in static posture.
01:36:27.360 --> 01:36:30.230
So, you have to measure the complete range
of motion of this hand when it stars from
01:36:30.230 --> 01:36:31.590
the up, from this point.
01:36:31.590 --> 01:36:37.360
So, if we consider, this as the initial position,
this one, then this the extension, then it
01:36:37.360 --> 01:36:39.930
is going upto this much flexion.
01:36:39.930 --> 01:36:49.080
In this way flexion-extension, abduction-adduction
and different types of measurements you can
01:36:49.080 --> 01:36:50.080
take.
01:36:50.080 --> 01:36:56.640
So, this is actually measured in the dynamic
posture, postural condition.
01:36:56.640 --> 01:37:02.670
Similarly, we can measure the reach-envelope,
that comfortable, while someone is seated,
01:37:02.670 --> 01:37:10.040
in the seated condition what is his total
reach area, mean, which area, which volume,
01:37:10.040 --> 01:37:14.070
around him that particular human can access.
01:37:14.070 --> 01:37:22.800
So, this reach-envelope, reach-zone, comfort
zone all these are described under dynamic
01:37:22.800 --> 01:37:24.260
anthropometric measurement.
01:37:24.260 --> 01:37:31.800
So, basic difference is that, in static it
is while, body is in static posture then the
01:37:31.800 --> 01:37:34.940
human body dimension measurement, that is
static body measurement and dynamic means,
01:37:34.940 --> 01:37:40.870
while even someone is leaning forward, one
step forward, leaning, bending, so, in during
01:37:40.870 --> 01:37:49.179
that time, the total area covered by the,
total distance covered by him, we can measure
01:37:49.179 --> 01:37:51.400
under dynamic anthropometric measurement.
01:37:51.400 --> 01:37:55.429
So, then it will be mentioned like, reach
zones, comfort zone, workplace envelope etcetera
01:37:55.429 --> 01:37:56.860
and measured under this dynamic.
01:37:56.860 --> 01:38:07.010
.
Now, if we look at this graph, this is adapted
01:38:07.010 --> 01:38:09.810
from Roebuck et al. (1975).
01:38:09.810 --> 01:38:18.620
So, here there are total 3 graphs; 1, 2, 3
and the lines, characteristics of lines are
01:38:18.620 --> 01:38:19.780
also defined.
01:38:19.780 --> 01:38:21.960
So, what is there?
01:38:21.960 --> 01:38:27.130
This is human, various human anthropometric
variables, stature means standing heights,
01:38:27.130 --> 01:38:31.090
sitting height, buttock-knee length, in this
way eye height.
01:38:31.090 --> 01:38:36.360
So, various different variables, anthropometric
variables are listed on X-axis,on Y-axis what
01:38:36.360 --> 01:38:44.810
is there, this is percentile value of USAF,
US air-force flying personnel their data collected
01:38:44.810 --> 01:38:46.320
in 1950.
01:38:46.320 --> 01:38:50.710
So, here actually three person’s data are
there.
01:38:50.710 --> 01:38:58.970
So, for one individual if you see, this is
the graph, for the third individual, this
01:38:58.970 --> 01:39:01.890
is the graph.
01:39:01.890 --> 01:39:05.940
So, what does it indicate?
01:39:05.940 --> 01:39:16.630
So, indicates that if we consider the first
person his stature is 78th percentile, for
01:39:16.630 --> 01:39:25.920
first person, the stature is 78th percentile,
but if you consider this one, his sitting
01:39:25.920 --> 01:39:28.750
height is actually 50th percentile.
01:39:28.750 --> 01:39:38.159
On the other hand, for the same person this
is knee height, sitting knee height, it is
01:39:38.159 --> 01:39:45.340
coming to say, for 15th percentile, so for
a single individual, these for this particular
01:39:45.340 --> 01:39:49.810
individual his different body parts are of
different percentile.
01:39:49.810 --> 01:39:51.949
So, how does it happen?
01:39:51.949 --> 01:39:59.889
So, if we measure the, for a population, if
we measure the height value of a population
01:39:59.889 --> 01:40:05.980
and if we find, say, my height value is 95th
percentile, it does not mean that if we measure
01:40:05.980 --> 01:40:12.780
the hand dimension of all the people and calculate
what is the percentile value of my hand length,
01:40:12.780 --> 01:40:19.300
it may not be 95th percentile, my head height
value or stature is 95th percentile, it does
01:40:19.300 --> 01:40:23.520
not mean that my hand length or leg length
will also be 95th percentile, it may be of
01:40:23.520 --> 01:40:26.960
different percentile.
01:40:26.960 --> 01:40:33.409
So, this is the typical example, is provided
by Roebuck et al., that for a single individual,
01:40:33.409 --> 01:40:40.250
for different body parts, it is the, percentile
value is actually different.
01:40:40.250 --> 01:40:49.410
If we consider the third person, in this case,
while his stature value is near about 28th
01:40:49.410 --> 01:40:55.120
percentile, but his hip-breadth sitting that
is coming to 58th percentile.
01:40:55.120 --> 01:41:02.320
So, although his stature is near about 28th
percentile of 50th percentile, but his, this
01:41:02.320 --> 01:41:06.880
point hip breadth sitting is coming to 58th
percentile.
01:41:06.880 --> 01:41:20.280
So, it clearly shows that, for a single individual
all the body dimension may not be of a particular
01:41:20.280 --> 01:41:27.020
percentile, if some one’s height or hand-length
is of at the particular percentile say, 98th
01:41:27.020 --> 01:41:28.020
percentile.
01:41:28.020 --> 01:41:37.510
It never means other body parts will be of
98th percentile or its nearby, it may be something
01:41:37.510 --> 01:41:39.050
different also.
01:41:39.050 --> 01:41:47.260
In reality, 5th percentile, in reality it
is almost impossible to find out the singe
01:41:47.260 --> 01:41:53.280
individual his whose all body dimension is
of a particular percentile value.
01:41:53.280 --> 01:41:57.659
This is very important statement, that in
reality, if we find for a particular individual,
01:41:57.659 --> 01:42:07.580
say if we take the example of me only then
as I already mentioned, my stature is 98th
01:42:07.580 --> 01:42:14.300
percentile it does not mean that my hand length
is also 98th percentile or my leg length is
01:42:14.300 --> 01:42:15.920
also 98th percentile.
01:42:15.920 --> 01:42:24.560
It may be, my hand length is 80th percentile,
my lower leg or my popliteal height it is
01:42:24.560 --> 01:42:27.250
just the 60th percentile.
01:42:27.250 --> 01:42:28.590
It may happen.
01:42:28.590 --> 01:42:35.340
In reality we cannot find any single individual
whose all body dimension is of a particular
01:42:35.340 --> 01:42:40.880
percentile value, but while we are creating
digital human model for virtual ergonomic
01:42:40.880 --> 01:42:47.120
evolution then we create this type of human
model, 5th percentile human model or 95th
01:42:47.120 --> 01:42:50.410
percent human model, what does it mean?
01:42:50.410 --> 01:42:56.060
In that case we consider such a situation
that all the body dimension have a particular
01:42:56.060 --> 01:43:01.690
percentile value, 5th percentile human model
mean all the body dimension of that particular
01:43:01.690 --> 01:43:10.070
human model of or digital human model is 5th
percentile value, but in reality it is not
01:43:10.070 --> 01:43:11.070
possible.
01:43:11.070 --> 01:43:15.200
But that type of optimal condition is considered.
01:43:15.200 --> 01:43:24.230
So, that all the 5th percentile anthropometric
data can be represented by creating that 5th
01:43:24.230 --> 01:43:42.929
percentile digital human model and that digital
human model we can use for various type of
01:43:42.929 --> 01:43:47.360
design dimension evaluation.
01:43:47.360 --> 01:43:53.670
Now, this is just one sample data, that how
the anthropometric data are represented.
01:43:53.670 --> 01:43:59.280
These are various measurements in body parts,
this is standing measurement, sitting measurement,
01:43:59.280 --> 01:44:01.050
sitting-standing combined measurement.
01:44:01.050 --> 01:44:07.510
So, stature, acromion height, elbow height
so different anthropometric variables are
01:44:07.510 --> 01:44:15.489
given here and this side mean value in bracket,
this standard deviation value, this is taken
01:44:15.489 --> 01:44:19.330
from anthropometric data of male agriculture
workers of Assam, India.
01:44:19.330 --> 01:44:25.850
So, agriculture worker of Assam state from
India, this value is taken and adapted from
01:44:25.850 --> 01:44:27.100
Patel et a.
01:44:27.100 --> 01:44:28.100
(2016).
01:44:28.100 --> 01:44:37.110
So, in from the data set, we can see that
if you consider stature or standing height,
01:44:37.110 --> 01:44:44.210
standing height is 1628 millimeter and now
first, this is calculated, as the data is
01:44:44.210 --> 01:44:45.210
following the normal distribution.
01:44:45.210 --> 01:44:51.550
So, we can calculate easily, first percentile,
5th percentile, 95th percentile and 99th percentile
01:44:51.550 --> 01:44:59.230
and those data we can use as per our requirement.
01:44:59.230 --> 01:45:05.650
So, this anthropometric data collection and
its percentile calculation is very important
01:45:05.650 --> 01:45:11.219
because while are designing any facility or
any product then we need to use this percentile
01:45:11.219 --> 01:45:20.330
anthropometric data to ensure human body dimensional
compatibility with the product’s physical
01:45:20.330 --> 01:45:21.330
dimension.
01:45:21.330 --> 01:45:29.420
For example if you see this particular research
work by Patel et al. differences in anthropometric
01:45:29.420 --> 01:45:37.570
data while Patel et al. discovering the comparison
of anthropometric data of Assamese agriculture
01:45:37.570 --> 01:45:44.830
workers with various region or anthropometric
data of various region of India as well as
01:45:44.830 --> 01:45:52.250
from other countries, then he mentioned, Patel
et al. then mentioned that, differences in
01:45:52.250 --> 01:45:58.740
anthropometric data within and between countries
indicate that simple adoption of agricultural
01:45:58.740 --> 01:46:05.590
tools and equipment for specific region might
lead to occupation hazard in target population.
01:46:05.590 --> 01:46:12.550
Because there is anthropometric variation
we can not blindly adopt product or equipment
01:46:12.550 --> 01:46:19.520
from one country to another because human
body dimension of that particular country
01:46:19.520 --> 01:46:28.929
is different from the country as where from
that product or that equipment or instrument
01:46:28.929 --> 01:46:37.170
has been imported say, for example, for Indian
population if we bring some product which
01:46:37.170 --> 01:46:45.110
is actually made for USA population, considering
the human body dimension of the USA population,
01:46:45.110 --> 01:46:54.650
if that product is directly brought to India
then that product may not be compatible with
01:46:54.650 --> 01:47:01.420
Indian human, Indian human body dimension
because that product actually has been made
01:47:01.420 --> 01:47:11.540
considering the anthropometric database of
the American population, that may not, that
01:47:11.540 --> 01:47:23.640
may not match with the anthropometric requirement
of Indian population.
01:47:23.640 --> 01:47:36.120
Now, if we discuss about the use of percentile
anthropometric data then first we discuss
01:47:36.120 --> 01:47:37.179
about this example.
01:47:37.179 --> 01:47:42.190
See, if we take this example of a bench, either
it may be primary school, for the primary
01:47:42.190 --> 01:47:48.960
school children this type of bench where 4-5
students are sitting together or this type
01:47:48.960 --> 01:47:55.350
of bench kept in the park that is actually
multiple user, many people can sit there.
01:47:55.350 --> 01:48:00.060
So, we can not specify for a particular individual.
01:48:00.060 --> 01:48:25.410
So, if we take the example of this bench for
a class room.
01:48:25.410 --> 01:49:06.239
So, if we want to design a bench for a class
room, we have to decide the leg height for
01:49:06.239 --> 01:49:07.739
that bench.
01:49:07.739 --> 01:49:11.870
So, design dimension is the leg height.
01:49:11.870 --> 01:49:16.840
For deciding that leg height, what should
be the leg height, what is the corresponding
01:49:16.840 --> 01:49:17.840
anthropometric variable?
01:49:17.840 --> 01:49:24.350
For that purpose anthropometric data or corresponding
human body dimension is lower leg height or
01:49:24.350 --> 01:49:29.080
popliteal height.
01:49:29.080 --> 01:49:38.280
Now, if it is asked as per which percentile
data of the popliteal height or lower leg
01:49:38.280 --> 01:49:42.240
height, this bench should be designed.
01:49:42.240 --> 01:49:48.210
So, that all student in the class room can
use comfortably.
01:49:48.210 --> 01:50:01.910
So, for this purpose we have collected anthropometric
data of the students, that variable is popliteal
01:50:01.910 --> 01:50:02.910
height.
01:50:02.910 --> 01:50:14.370
So, we have measured the popliteal height
of the students, from that we have calculated
01:50:14.370 --> 01:50:25.290
50th percentile data, 5th percentile, 50th
percentile and.
01:50:25.290 --> 01:50:28.020
Okay, anyway.
01:50:28.020 --> 01:50:48.640
So, it will be easier to show here, say for
example, for that class room we have to calculated
01:50:48.640 --> 01:50:58.940
the popliteal height and the popliteal height
we have measured and calculated 5th percentile,
01:50:58.940 --> 01:51:06.520
then 50th percentile and 95th percentile.
01:51:06.520 --> 01:51:13.170
So, we know the popliteal height of the students
and we have calculated 5th ,50th and 95th
01:51:13.170 --> 01:51:14.210
percentile popliteal height.
01:51:14.210 --> 01:51:16.410
Now you want to design a bench.
01:51:16.410 --> 01:51:25.850
Now, the question is which percentile data
we will you use for this purpose.
01:51:25.850 --> 01:51:40.520
You want to design the leg height of the bench,
if we ask which percentile of popliteal data
01:51:40.520 --> 01:51:50.889
should be used for deciding the height of
this bench, if you we answer it is 5th percentile,
01:51:50.889 --> 01:51:51.909
then what is happening?
01:51:51.909 --> 01:52:01.810
One fifth percentile student, he is sitting
comfortably, his leg is exactly matching with
01:52:01.810 --> 01:52:03.469
the height of the bench.
01:52:03.469 --> 01:52:09.920
So, the 5th percentile student is sitting
comfortably because this height, this popliteal
01:52:09.920 --> 01:52:16.090
height is matching with the bench height,
it is designed as per his popliteal height.
01:52:16.090 --> 01:52:17.949
So, 5th percentile student is comfortable.
01:52:17.949 --> 01:52:23.139
So, what about 50th percentile, while the
average student with average dimension is
01:52:23.139 --> 01:52:32.260
coming, he actually facing the problem of
raised knee, then if 95th percentile students
01:52:32.260 --> 01:52:39.850
come, then his popliteal height is more, he
is also facing the problem of raised knee.
01:52:39.850 --> 01:52:47.000
So only if we design this one as per the 5th
percentile popliteal height data, then 5th
01:52:47.000 --> 01:52:54.520
percentile is comfortable, but 50th percentile
and 95th percentile is not comfortable.
01:52:54.520 --> 01:53:03.090
Similarly, next, no then we will design as
per the 50th percentile, if we design as per
01:53:03.090 --> 01:53:10.550
the 50th percentile what will happen?
01:53:10.550 --> 01:53:21.300
As we have designed as per the 50th percentile.
01:53:21.300 --> 01:53:31.700
So, 50th percentile student his popliteal
height is exactly matching, he is his sitting.
01:53:31.700 --> 01:53:40.430
He is sitting comfortably, it is exactly matching
with his popliteal height.
01:53:40.430 --> 01:53:50.780
Now, one 5th percentile student is sitting
there, his leg is hanging.
01:53:50.780 --> 01:53:58.840
So, this is designed as per the 50th percentile
popliteal height, so 50th percentile student
01:53:58.840 --> 01:54:03.830
is comfortable, but 5th percentile student,
here it is 5th percentile, 5th percentile,
01:54:03.830 --> 01:54:05.219
50th percentile, 95th percentile.
01:54:05.219 --> 01:54:09.469
In this case while we design as per the 5th
percentile popliteal height then 5th percentile
01:54:09.469 --> 01:54:16.000
student is comfortable, but 95th percentile,
there are 50th percentile, there is raised
01:54:16.000 --> 01:54:22.619
knee, and for 95th percentile there is more
raised knee situation.
01:54:22.619 --> 01:54:28.619
In case of, while we are using 50th percentile
data for deciding the leg height, while we
01:54:28.619 --> 01:54:35.570
are are using 50th percentile popliteal height
for deciding the leg height of the bench,
01:54:35.570 --> 01:54:41.610
then 50th percentile students are comfortable,
but 5th percentile their leg is hanging and
01:54:41.610 --> 01:54:54.920
in case of 95th percentile there is a problem
of raised knee, so they are facing the problem
01:54:54.920 --> 01:54:58.100
of raised knee because this is small, in case
of them.
01:54:58.100 --> 01:55:02.389
So, they are not comfortable, they are also
not comfortable.
01:55:02.389 --> 01:55:23.460
In the third scenario, if you design the bench
height as per 95th percentile, then what will
01:55:23.460 --> 01:55:32.420
happen, 95th percentile?
01:55:32.420 --> 01:55:40.909
Then 95th percentile students with bigger
body dimension, they are sitting comfortably.
01:55:40.909 --> 01:55:49.100
It is matching with their body dimension,
this height, but they are comfortable.
01:55:49.100 --> 01:55:57.369
But 50th percentile, their leg is hanging
and 5th percentile their leg is also hanging.
01:55:57.369 --> 01:56:00.570
So, they are not comfortable.
01:56:00.570 --> 01:56:06.260
So, which percentile data of the popliteal
height should we use, so, that most of the
01:56:06.260 --> 01:56:09.550
student can sit comfortably.
01:56:09.550 --> 01:56:17.350
So, actually answer is 50th percentile, because
in every case if we use 5th percentile popliteal
01:56:17.350 --> 01:56:24.449
height data then 5 percent, 5th percentile
students, with 5th percentile popliteal height
01:56:24.449 --> 01:56:31.490
value, he or she is comfortable, but the students
with higher percentile popliteal value they
01:56:31.490 --> 01:56:32.679
are not comfortable.
01:56:32.679 --> 01:56:37.790
If we use 50th percentile data, then 50th
students, students with 50th percentile popliteal
01:56:37.790 --> 01:56:44.030
height he or she is comfortable and other
students around that 50th percentile value
01:56:44.030 --> 01:56:49.730
they are, maybe comfortable, but the students
at the lower end, that is 5th percentile or
01:56:49.730 --> 01:56:52.180
95th percentile popliteal height value they
are not comfortable.
01:56:52.180 --> 01:56:56.730
Similarly, while we are using 95th percentile
popliteal height value for designing this
01:56:56.730 --> 01:57:05.219
leg height of the bench, then student with
95th percentile value, 95th percentile popliteal
01:57:05.219 --> 01:57:14.280
height value, they are comfortable, but for
50th percentile and 5th percentile, their
01:57:14.280 --> 01:57:17.070
leg is actually hanging.
01:57:17.070 --> 01:57:23.800
So, what should we do to accommodate more
number of people, wide range of students,
01:57:23.800 --> 01:57:24.800
what should we do?
01:57:24.800 --> 01:57:26.810
We should go for 50th percentile.
01:57:26.810 --> 01:57:28.910
Why 50th percentile?
01:57:28.910 --> 01:57:36.600
Because if we use 50th percentile popliteal
height value, then what will happen, good
01:57:36.600 --> 01:57:44.980
number of students will be accommodated whose
value is nearby 50th percentile, they will
01:57:44.980 --> 01:57:45.980
accommodated.
01:57:45.980 --> 01:57:52.969
If we consider, if we recall that earlier,
the answer is lying here if we look at the
01:57:52.969 --> 01:57:54.290
normal distribution curve.
01:57:54.290 --> 01:58:01.380
So, normal distribution curve, if we use 50th
percentile data then most of the students’
01:58:01.380 --> 01:58:08.550
popliteal height value from that mean value
or the 50th percentile value, most of the
01:58:08.550 --> 01:58:13.900
students popliteal height value or leg height
value will be nearby that value.
01:58:13.900 --> 01:58:20.449
So, good number of students will be able to
accommodate that one, because in any population,
01:58:20.449 --> 01:58:26.580
for anybody dimension most of the people,
for the particular body dimension more number
01:58:26.580 --> 01:58:30.850
of people belongs to nearby average value.
01:58:30.850 --> 01:58:38.980
For that reason, if we design that bench height
according to 50th percentile popliteal height,
01:58:38.980 --> 01:58:45.840
then obviously, 50th percent students, students
with 50th percentile popliteal height they
01:58:45.840 --> 01:58:55.600
can sit comfortably and also students with
little bit more popliteal value, for example,
01:58:55.600 --> 01:59:01.949
say 60th percentile or 40th percentile, they
can also sit with little bit discomfort.
01:59:01.949 --> 01:59:14.699
But if we use 5th percentile popliteal height
for designing the bench then what will happen?
01:59:14.699 --> 01:59:19.440
But if we use 5th percentile popliteal height
for designing this bench, then only 5th percentile
01:59:19.440 --> 01:59:25.550
students, their number in, mean students with
the lower body dimension lower popliteal height
01:59:25.550 --> 01:59:27.679
value, their number is less.
01:59:27.679 --> 01:59:33.580
From the normal distribution curve, it clearly
mentions that, if we see the normal distribution
01:59:33.580 --> 01:59:44.790
curve, more number of people are nearby the
mean value, this mean value is 50th percentile
01:59:44.790 --> 01:59:46.330
value.
01:59:46.330 --> 01:59:55.870
But less number of people are there towards
5th percentile or 95th percentile.
01:59:55.870 --> 02:00:06.500
So, more number, from this bell shaped curve
it appears that more number of students popliteal
02:00:06.500 --> 02:00:12.550
height value is concentrated around the mean
value, so if we use 50th percentile popliteal
02:00:12.550 --> 02:00:19.889
data for this purpose, then good number of
students will be accommodated, whose value
02:00:19.889 --> 02:00:23.040
are nearby the mean value or the 50th percentile
value, but if we use 5th percentile popliteal
02:00:23.040 --> 02:00:30.660
height, 5th percentile, then only the few
number of people who are at the lower side,
02:00:30.660 --> 02:00:38.179
they will be accommodated, but good number
of students whose leg height, leg height or
02:00:38.179 --> 02:00:43.300
popliteal height value is more than the 5th
percentile they will not be comfortable to
02:00:43.300 --> 02:00:44.300
sit.
02:00:44.300 --> 02:00:55.670
One the other hand, if we use 95th percentile,
then 95th percentile, at this side in higher
02:00:55.670 --> 02:01:02.650
percentile value, only few students nearby
that they may be accommodated there, but good
02:01:02.650 --> 02:01:09.880
number students whose leg height or popliteal
height value is less than the 95th percentile,
02:01:09.880 --> 02:01:16.550
starting from the 95th percentile to first
percentile, this wide range of students, they
02:01:16.550 --> 02:01:21.170
will find difficulty to accommodate this,
to use this facility.
02:01:21.170 --> 02:01:25.010
So, for this purpose, also, what is the final
answer?.
02:01:25.010 --> 02:01:30.810
Final answer is, we will use 50th percentile
popliteal height value for designing this,
02:01:30.810 --> 02:01:33.270
deciding this leg height of the bench.
02:01:33.270 --> 02:01:40.070
So, that 50th percentile student, 50th percentile
student with 50th percentile popliteal height
02:01:40.070 --> 02:01:48.989
value will be, can sit comfortably and all
other students whose popliteal height value
02:01:48.989 --> 02:01:56.190
is nearby 50th percentile, they will also
be able avail to use that facility and their
02:01:56.190 --> 02:01:57.480
number is also big.
02:01:57.480 --> 02:02:04.730
Because as we mentioned, from the bell shaped
curve, data in normal distribution pattern
02:02:04.730 --> 02:02:10.950
is concentrated around the mean value and
as we are moving away from the 50th percentile
02:02:10.950 --> 02:02:17.580
value, the number of, the frequency is gradually
or the number of individual in that side is
02:02:17.580 --> 02:02:19.810
gradually decreasing.
02:02:19.810 --> 02:02:24.280
More number of people are concentrated around
the mean or the 50th percentile value.
02:02:24.280 --> 02:02:30.020
In both the end it is gradually reducing.
02:02:30.020 --> 02:02:32.810
Then what will we you do?
02:02:32.810 --> 02:02:42.369
So, we will use 50th percentile for this purpose,
then what will happen for others, in this
02:02:42.369 --> 02:02:49.800
case, for, we are using 50th percentile, so
50th percentile can sit, for 5th percentile
02:02:49.800 --> 02:02:53.190
or for lower percentile, we will position
a footrest.
02:02:53.190 --> 02:02:56.659
So, we will design a footrest.
02:02:56.659 --> 02:03:05.530
So, students with lower popliteal height value,
they can sit with lower popliteal height value
02:03:05.530 --> 02:03:09.469
they can sit by keeping their leg on the footrest.
02:03:09.469 --> 02:03:17.400
On
the other hand, what about the 95th percentile?
02:03:17.400 --> 02:03:23.630
For 95th percentile, we have to suggest them,
keep their leg forward.
02:03:23.630 --> 02:03:35.050
So, that they can accommodate their leg, they
can rest their thighs on the seat and they
02:03:35.050 --> 02:03:37.850
can extend their leg forward.
02:03:37.850 --> 02:03:39.199
So, this is possible.
02:03:39.199 --> 02:03:43.530
So, instead of raised knee they can extend
their leg in forward direction.
02:03:43.530 --> 02:03:48.280
For that purpose, we have to make it sure
that there is sufficient space.
02:03:48.280 --> 02:03:51.201
So, that leg stretching in forward direction
is possible.
02:03:51.201 --> 02:04:05.670
With the same line, another question, if we
consider the sofa, while we are designing
02:04:05.670 --> 02:04:08.239
the sofa, then what we do?
02:04:08.239 --> 02:04:09.239
Then?
02:04:09.239 --> 02:04:17.010
Generally, sofa, height of the sofa is less,
why, because it is assumed that while people
02:04:17.010 --> 02:04:22.990
are sitting on the sofa, their posture will
be relaxed posture, while there is relaxed
02:04:22.990 --> 02:04:33.000
posture and people will, his angle is almost
120 degree or 100 degree, then they will extend
02:04:33.000 --> 02:04:34.239
their weight forward.
02:04:34.239 --> 02:04:40.150
So, enough height is not required, for that
purpose, because who is using that sofa, who
02:04:40.150 --> 02:04:46.710
is sitting on that sofa he or she will extend
his leg forward due to his body bending backward,
02:04:46.710 --> 02:04:57.840
while this angle, generally it happens, when
this angle is coming more than 100 degree,
02:04:57.840 --> 02:05:33.129
automatically, this angle will become 100
degree or 120 degree.