Lesson video

Hi there.

My name is Mr. Booth, and I'm delighted that you're joining me today for this design and technology lesson.

We are gonna be looking at anthropometrics and ergonomics, and how those two are related, and how designers use anthropometric data to design ergonomic products.

This lesson is part of the Product analysis unit, with a context encouraging healthy lifestyles.

So let's look at what we're gonna be doing today.

By the end of today's lesson, you will be able to identify and interpret anthropometric data and design ergonomic products.

We have a few keywords today.

Of course, anthropometrics is one of those keywords, which we will keep revisiting throughout the lesson, and this is simply the measurement of people.

Part of anthropometrics is the data that we collect.

And when we look at that data, we can create things called bell curves.

We'll come back to this a little bit later on, but this is basically a symmetrical line representing a normal distribution of data.

We also have what we call a percentile.

Now this is really important for designers.

It's simply a part of a dataset after splitting the data into 100 equal parts, a percentage, but we need to use certain percentiles when we are designing products.

And then finally, once we have taken all that anthropometric data, we have analyzed it and interpreted it, we then need to design products that are ergonomic.

And ergonomics is, of course, the interaction of people and the products that they use.

There are three learning cycles for today.

The first one is types of anthropometric data.

So let's go and have a look at what they are.

So as we know from the keyword slide, anthropometrics are the measurement of people.

So where does this word actually come from?

Well, it comes from the Greek word for anthropo, which means human, and metrics, which means measure.

So human measure, or human measurement, and what we call that is the measurements of people.

When we collect that as a dataset, we call this anthropometric data of people, and that's what we use in the design of products.

Now, anthropometric data is the collection of data relating to the measurement of people, but we can actually look at some examples of that as well.

What do we actually mean by that?

Well, of course, a really easy one to look at is the height.

In here, we have Sam, and we can look at the height of somebody that might be important when you are designing a product.

We could look at things like hand span.

So if you are designing a product that interacts with a user's hands, you'll probably need to know the size of their hand span.

We also have things like head circumference.

If you're designing a helmet, for example, you might need to know the circumference of people's heads.

And then finally, we have also things like grip strength, very important for products that we use in our kitchen, for example, where we are using our hands a lot.

Now, when designing, as you all will know, it is important to be inclusive and consider all different users.

And anthropometric data represents and reflects society and includes data from different ages, genders, and ethnicities.

And it's important to remember that.

And designers, as designers, you and me as designers, we will select the relevant data for the product that we are designing, the product that we are going to design.

So let's have a quick check for understanding to see if you've been listening.

So anthropometric data is the measurement of parts, of products, or of people?

A nice easy one to start off with.

It is, of course, the measurement of people.

Well done.

Okay, so let's move on.

So there are many different measurements of a human body.

Absolutely loads.

And depending on the products that you are designing will depend on which of those measurements you actually need to consider.

The measurements can be divided into two categories, broadly into two categories.

The first one being fixed, fixed measurements.

Now these are the measurements of one point on your body, on the body to the other.

Now I know you are all growing and your height will get, you will all get probably a little bit taller.

But actually, if I took a measurement of your height now and then we took one tomorrow, the chances are it's gonna be very similar.

So we call these fixed measurements.

And they could be height, which we've looked at, hand span, we've looked at as well, but they can also be things like eye level.

That might be quite important when you're designing a product, or including things like user strength.

Now the other category we can divide, we can divide out, we can divide measurements into is, of course, ranges of motion.

And this refers to the full movement of a joint or a body part, or something like that.

For example, head rotation angles.

So quite important there's some products to know how people can turn their heads.

It could be things like line of sight, quite important as well when designing products.

And forward reach, knee lift, or even things like foot rotation when people are interacting with certain products.

So we have fixed category, the fixed category, and we also have the range of motion.

Quick check for understanding.

The measurement of how far you can reach above your head with your arms is a fixed measurement.

Is that true or is that false?

It is, of course, false.

But can you explain why?

Have a go at doing that.

Well, I'm sure got right that right.

Reaching your arms above your head is a range of motion.

Of course it is, okay?

It's not fixed.

So it's a range of motion.

I know you're seeing how far you can be stretching up.

So if you're using a product which you have to do that could be a piece of gym equipment, then, obviously, the range of motion is important to consider.

So designers, you and me as designers, we need to identify the relevant anthropometric data that is needed when designing specific products.

So for example, if we were going to design a bicycle, what anthropometric data needs considering when designing a bicycle?

Have a think about it.

So what I would say is I would say for the head, we need to think about rotation.

Okay, the head rotation, we talked about that previously, haven't we?

And also things like line of sight, a downward line of sight when you're cycling along.

Hands and arms, vital to be able to ride a bike, ride a bicycle where you hands your arms.

So you could think that things are like forward, grip strength, even like you know how much your elbow will bend.

We could have the body.

So your torso length, that obviously relates very much to frame sizes.

And also things like, you know, backwards reach and things like that that you might need to be able to do to either get a drink or get something out the back of the bike.

Then you also also have your legs, what powers the bicycle.

So it's important to know all the anthropometric data about the legs as well.

I'm sure you also got quite a few of those.

So let's have a look at this product now.

So a quick check for understanding.

So here we have a stapler.

So which piece of anthropometric data is most relevant for this product?

Now most relevant, so the others might be relevant, but which is most relevant?

What do you think?

Is it grip strength, elbow height, downward sight range, or forward reach?

It is, of course, grip strength.

I'm sure you've got that.

And that's important to actually make the mechanism work and actually fire those staples out through the paper.

Okay, so onto your first task.

Now we've looked at anthropometric data.

So the class of decided to source some new safety glasses for the workshop.

So what I would like you to do is, first of all, identify at least three anthropometric pieces of data that would be needed to help the class source new safety glasses.

So we are thinking about those safety glasses about what we've just talked about, and I want you to identify at least three pieces of optometric data.

Then what I would like you to do is justify your answers for choosing those pieces of data.

Why have you chosen those?

Pause the video.

Once you've done that, come back and we'll look at some model answers.

Okay, so let's have a look at what you might have written down.

So first of all, here we have Jun, and what he's decided to do is he said the measurement from your ear, okay, to the front of your face.

Okay, so this is called your temple length, and that's a really important measurement.

And Jun has justified that by saying, "This will give us more information about how long the arms of the glasses need to be to suit the class.

" And as you can see, the current arms on Jun's ears there are too long, which, of course, means that they slip forward when wearing them.

Not only this is this uncomfortable, but he's also said that this is a safety hazard as well.

So that's a great answer there from Jun.

We've also got Izzy.

Now what Izzy has decided to talk about is the measurement from one side of your face to the other.

This is, of course, called your head width.

So you might have written that down.

And this will give you more information about how wide the lenses on the glasses should be.

If they're too big and heavy when wearing them, they might obscure vision when using machines, which again is a health and safety risk, but also they might distract you and they will be uncomfortable.

And then finally, Laura has decided to focus on the higher part of your nose when wearing a glasses.

This is where the glasses rest.

This is called a bridge, the bridge measurement.

And this, of course, will give you more information about the gap that the lenses need to be, the bit between the glasses and where it sits on your nose.

If it's too small, then glasses can be quite uncomfortable to wear.

Some great answers there.

I'm sure you've got the same ones as well.

Well done.

Let's move on to the next learning cycle.

So now we've got, we now we know, sorry, now we know the types of anthropometric data, we now need to present and interpret that data in some way, so as designers, we can use that to inform our designs.

So first of all, here we have some height data for pupils in a class.

So this is the Oak class, and here are the measurements in millimeters.

So you can see we have a range of heights throughout the class.

And what we can do is we can actually then construct a table out of that.

And that's quite a nice way of presenting that data.

But we've got five pieces of data here of all the pupils in the class.

But actually, if we think about that, in order to get an accurate anthropometric dataset, we need a bigger sample size.

So what we're gonna do is actually use 25 pupils from the class, and we're gonna take all their height measurements and use those.

I'm not gonna present that on the screen right now 'cause that would just simply be too much data, but you, obviously, need to have a good sample size.

So what we're gonna interpret it as is we're gonna interpret that as this, and this is a graph which we call a histogram.

Now you might have seen this in your science and math lessons.

So what we've done is we've presented it in a slightly different way.

But what this does is this helps to inform designers of key pieces of information to help support our designing.

So let's just have a look at the histogram a little bit closer.

So first of all, we have these columns, and we call these bins, 'cause they look a little bit like bins and we put some data in the bin.

A histogram can also inform us of how many of something fall between two boundaries in a dataset.

Now we call this the frequency.

So let's have a closer look at what I mean by that.

So let's take this bin here.

This bin has nine people in it, and they fall between the boundaries are 1,501 and 1,600 millimeters in height.

And we can quite clearly see there are nine people in this bin.

For this bin, you can see there are five people, and they fall between the boundary of 1,601 and 1,700 millimeters.

We can also look at what we call the range.

This is the spread of data.

And for this, it's the maximum height minus the minimum height.

And for this class, that's around 1/2 meter or 500 millimeters.

So let's have a quick check to make sure that you understood that.

The amount of something that falls between two boundaries on a histogram is called the dataset, range, frequency, or sample size.

It is, of course, the frequency.

Well done.

So what we can also look at this, what we can also do with the histogram is we can add what we call a line of normal distribution.

And you can see we've added it here in red.

When the spread of data is evenly distributed, this line is symmetrical.

The line is commonly known as a bell curve due to, of course, it resembling a bell.

Now, if you were in a class of pupils and you took all the dataset of all their height, it would be quite likely that you would see something very similar to what we have got here.

And a bell curve shows that most values of the dataset will cluster around the average, which is in the middle because it's the average, and they will reduce in frequency towards the high and the low extremes on either side of this.

The tip of the bell curve is the average value of the dataset, and that's obviously in the middle, and this tells designers the average measurement of the data, which can be used quite useful in itself.

Another quick check for understanding.

True or false?

The line of normal distribution, the bell curve which we've just talked about, is symmetrical.

Is that true or false?

It is, of course, true.

Can you explain why?

Well, of course, when the spread of data in an anthropometric dataset is evenly distributed, that's the key kind of aspect you wanna think about there on a histogram, the line of normal distribution, which is commonly in alone as the bell curve, is symmetrical.

That's what we've just talked about.

Well done.

So now we're gonna talk about a percentile.

So you know what a percentage is out of 100.

So a percentile is one part of a dataset after splitting the dataset into 100 equal parts, and this will equate to a certain value.

There are three important percentiles to consider when interpreting anthropometric data for us as designers.

That is the 5th percentile, the 50th percentile, and the 95th percentile.

So if we go back to our histogram, we can see right in the middle there, we have the 50th percentile, which is the average.

Okay?

The 50th percentile in this case is 1,545 millimeters in this example.

And we can take that from the data range that we've got.

Another quick check for understanding, what are the three important percentiles in anthropometric data?

Are they the 5th, the 50th, the 90th, the 5th, the 75th, and the 95th, or the 5th, the 50th, and the 95th percentile, Did well to get that, didn't I?

It is, of course, C, the 5th, the 50th, and the 95th percentile.

Okay, so let's have a closer look at the 5th and the 95th percentile then.

So if we look at the 5th percentile, we can see that, obviously, 5% below that, obviously, is the smallest 5% of measurements.

And the same goes for the 95th percentile.

This tells us, obviously, in this case, the heights are the tallest or the largest measurements in the dataset and the 5% of that.

Okay, so another quick check for understanding here, but a bit different this time.

What I would like you to do is label the parts of the histogram that are missing.

So we've just looked at all these, can you label the four parts that are missing.

And we pointed to them as well.

Have a go at that.

Okay, let's see how you did.

You should have got the 5th percentile, the 95th percentile, the bell curve, and, of course, the 50th percentile.

If you've got all four of those, well done, that's brilliant.

If we only focused on the 50th percentile of data, the average, we would exclude the majority of users in the dataset.

So what we need to do is we need to use a bigger range.

We need to use the data from the 5th, the 95th percentile.

That include 90% of users that we will be able to consider when designing.

If you think about designing a doorway, if you designed a doorway for the average person, the average person in your dataset, then everyone above that height will bang their head as they walk through that doorway because it'll be too small for them.

So that's why we have to include from the 5th to the 95th percentile to make sure we've got 90% of users.

So let's have a look at that back on our histogram.

So there we have the 5th percentile and the 95th percentile.

And in between that, we have the majority of users which we can then use when we are designing our products.

Now, products that allow users to adjust their positions are really good examples of designers considering the anthropometric data between the 5th and the 95th percentile because this includes 90% of users.

Now, can you think of any products that are adjustable that allow you to adjust depending on the person that is using them?

Okay, one example might be adjustable car seats, and you can adjust these in all manners, height, the length from the pedals of the car, the back rest you can adjust all sorts, and that again is so multiple drivers, multiple passengers can sit comfortably in the car.

Another good example is adjustable gym machines, which can be adjusted depending on the person using those machines.

I'm sure your examples were brilliant as well.

Okay, so we're now onto task B.

So what I want you to do for task B is the first thing is I want you to explain what information the 50th percentile tells designers.

That's the first thing I want you to do.

Then what I want you to do is mark on this graph, you can see it's slightly different, but it's the same principle, it's still a histogram.

I want to mark on the graph where the 50th percentile is and give an approximate value of what that 50th percentile is.

And that can be approximate 'cause it's sometimes quite hard to read that on a histogram.

And then finally, I want you to tell me what is the significance of the data between the double-headed arrow.

So where those two dotted green lines are, what is the significance of that data between there?

We've just spoken about it.

So have a go at that.

Okay, so let's have a look at some sample answers that you might have done.

So here we have Lucas, and he says, "The 50th percentile is the average value of the height set of data, of the dataset.

The designer will know the average height of pupils in the class.

" And also this might be a good starting point for, you know, initial prototyping stages, depending on the product you're designing.

He's then answered the second part, which is the 50th percentile is the central line and the tip of the bell curve.

So where that tip of the bell curve is, that is the average.

And the approximate value that Lucas managed to find is 1,540 millimeters.

We then have Jacob doing the third part of this.

So the significance of the arrow is the range of data from the 5th to 95th percentile.

This covers the range of heights for 90% of the pupils in the data.

This information supports designers as 90% of users are included in this dataset, in this research for the designing phase.

It's important for designers to be inclusive and consider all different users when designing products.

And by including 90% from the data range, we can do that.

Well done.

That's brilliant.

Okay, so we're now onto the final learning cycle in this lesson.

This is anthropometric data to support designing.

So how can we use all this data?

All this data we found out, we know how, we know what type of anthropometric data is, we can now present it and we can interpret that data.

How do we then use that to design products?

Let's have a look.

So designers use anthropometric data to support designing products that are most suitable in size and weight for users, ensure users have the best user experience with a product.

That's a really important part, isn't it?

Help reduce the risk or injury, a misuse of the product.

That's an important part as well, which we need to use anthropometric data for.

Okay?

And all of this is known as ergonomics, that interaction or a product with its users.

So anthropometric data can be used to design ergonomic products, and there's your link.

So let's just check if you've been listening.

Designers use anthropometric data to support designing sustainable products, colourful products, reusable products, or ergonomic products.

A nice easy one to start this learning cycle.

It is, of course, ergonomic products.

Well done.

So we know that ergonomics is the interaction of people and the products that they use.

We know that from the keyword slide that we talked about.

And the interaction with the products by a user can include lots of different factors, and we are gonna look at some of them specifically.

We're gonna look at comfort, the ease of use, the colour, you might not think that, but the colour is actually quite important as well.

I'll show you some examples in a minute.

The size, the weight, and also the symbols that are used within a product.

And a really good example of that is games consoles' controllers.

If you think about everything in that list there, they have been considered in the design of games controllers.

So let's have a look at a vacuum cleaner.

So again, we're gonna look at that list and we're gonna see, we can pick apart this vacuum cleaner to see how it's been designed.

So if for comfort, is the product comfortable to hold and easy to move?

So think about a vacuum cleaner, you've gotta pick it up by the handle.

Okay, you've gotta move it around.

That's really important as well.

Okay, you've gotta be able to get under tables and things like that.

Have the designers thought about that when they're thinking about things like handles?

Ease of use and symbols.

How difficult is the product to use or understand?

Have the correct symbols been used so you can recognize those symbols without having to read an instruction manual every time to be able to use that product?

Colours.

Do the colours of the product influence how the product is used or understood?

Now this vacuum cleaner has some very bright colours on, and that's for a reason.

It's to attract our attention to certain parts of that product because they will either extend a hose, like extend one of the hoses, or it might be to to empty the vacuum cleaner.

And then finally, size and weight.

Does it fit with the user?

Does it fit in the cupboard that the user stores in?

Can the user lift it and move it?

Can they carry up the stairs?

All these different parts of anthropometric data the designer has to consider to be able to design an ergonomic product.

So quick check.

Ergonomics is the understanding of why people use products, the interaction between people and products, the study of measurements of products.

It is, of course, B, the interaction of people and product, between, sorry, people and products.

So we know now that designers use anthropometric data to support designing products that are most suitable in size and weight for users.

So let's have a closer look at size and weight for users.

And we're gonna use an office chair, simple office chair, okay, that you might be sat in right now as an example for this.

So, Sofia has said, "Anthropometric data has been used to determine the size of the different parts of the chair.

This includes the length and width of the back and armrests.

" So that's how this designer has considered size and weight.

Okay, the adjustable features for the height of the seat takes into account the range of data from the 5th to the 95th percentile.

This is determined how low and high the seat can move.

So a number of different users can use that seat, and that's what Andeep has told us.

We then have Izzy, "It has wheels.

" And that's fantastic because this means that the user can move the chair easily when needed and will not have to lift the heavy chair, which could, of course, result in injury.

They could be sat in the chair and move it, or they could stand up to move it.

So that's a really good answer from Izzy.

Okay, so what about user experience?

So designers use anthropometric data to ensure users have the best, users have the best user experience with the product.

So here we have a Aisha, and she said that adding adjustable features to suit the user's different heights and sitting positions improve the user experience.

Adding soft materials and materials that allow for ventilation along with the padded seat and the padded seat for comfort are further supporting this.

And that's all about the user experience to make you comfortable using this product.

So what about risk then?

So designers use anthropometric data to help reduce the risk or injury of, sorry, risk of injury or misuse when looking at when designing, when using a product.

Let's get that right.

Now Alex here has said, "A chair on is on wheels, which means it won't have to be lifted when it's moved.

This can minimize injuries to your back.

" And Sam, of course, says, "Adjustable height to positions feature ensure that the chair can be set to the correct posture for the user.

This can avoid injuries to your wrist, back, and neck.

" And that's very important if you're sat down for a long time.

Okay, so onto the final task.

So what I want you to do first is identify the anthropometric data needed to support the designing of a kettle.

And what we're talking about is a kettle here with a separate base.

So it's got a separate base that you put it on when you are boiling it.

So that's the first thing I want you to do.

Then, I'm sorry, while you are doing that, I want you to consider the following in your answer.

I want you to consider the size, the weight, the user experience, and the risk of injury.

Think about the desk chair example that we've just gone through.

Once you've done that, I want you then to explain how the anthropometric data you have identified has been used in the design of the kettle.

And again, consider the following in your answer, the size, the weight, the user experience, and the risk of injury.

Remembering to think back to the example of the desk chair.

Have a go at that, see how you get on, and come back and we'll look at some model answers.

Okay, so your answers could include this.

So let's see what Laura has come up with.

So Laura has said, "The anthropometric data needed when designing the kettle would be hand sizes, hand span, and grip strength.

It would also include data about what range of weight people can lift with one hand and the range of movement in the wrist and the elbow can rotate at.

" That's really important, isn't it?

It's no point in designing a massive kettle that fills with water that people can't lift.

"There should be consideration of eyesight lines and knowledge about the caution and warning of colours as there is a safety risk involved in this activity when burning, with if they," sorry, "they could get burnt when boiling water or steam.

" So that's really important to think about as well.

So great answer from Laura there.

So let's see what Jun has put.

So Jun has said, "The data is used to support the design of the handle.

It is a loop shape which provide a space for your hand to pass through.

The space allows for a range of hand sizes whilst the handle is being attached to the kettle on both ends for stability and strength.

" Great answer there on why that anthropometric data has been used.

"Knowing about the angles of the wrist and the elbow and the shoulder, and the shoulder can, and what the shoulder can rotate at can determine where the spout is positioned.

" That's very important as well.

When you're pouring a kettle, how far you actually have to tip your arm over and your elbow and your shoulder to be able to do that.

It's shape and how long it needs to be.

That's important, isn't it?

Yeah.

"Avoiding spilling or splashing hot water is vital in minimizing accidents or injury.

" So we've related that to risk.

So great answer from Jun there.

Let's see what Lucas has said.

Okay, so this is Lucas for the second part of it.

"So grip strength and weight data will determine the size of the main body and the amount of water in the kettle can hold in full.

" That's important, isn't it?

We talked about that.

"There is a clear bar that allows you to see how much water is in the kettle before you lift it and how much to refill it to avoid injury or accident, and also how much water you actually need to boil.

The kettle is a separate base.

This allows you to remove it when filling up and pouring it," which means you don't have to pull a a cable out the back, the old kettle lead style kettles.

"This provides an ease of use and knowing weight and grip strength data is vital to this process.

" So great answer from Lucas there.

Brilliant.

We've made it through to the end of the lesson.

You've been absolutely fantastic.

And now, of course, you know the interaction of anthropometric and ergonomics and how we can use that data to design better for our users.

Let's have a quick summary of the lesson.

So first of all, anthropometrics are the measurement of people.

When this data is collected, it's called anthropometric data.

There are many types of anthropometric data, from height to eyesight levels.

Anthropometric data is collected and then presented.

This is displayed in a histogram in this example, with a symmetrical line called a bell curve.

This provides information for designers about the 5th, the 50th, and the 95th percentiles.

This information is used to help designer s create ergonomic products.

Considering ergonomics when designing can increase the success of the user experience with a product, which, of course, is what design and technology is all about.

Well done today.

It's been brilliant being with you.

I'll see you again soon for another design and technology lesson.

Bye-bye.