Optimization of Sitting Posture Classification based on

Anthropometric Data

Leonardo Martins

1,2

, Bruno Ribeiro

, Rui Almeida

, Hugo Pereira

, Adelaide Jesus

1,3

Cláudia Quaresma

1,3

and Pedro Vieira

1,3

Department of Physics, Faculdade de Ciências e Tecnologias, Universidade Nova de Lisboa,

Quinta da Torre, 2829-516, Caparica, Portugal

UNINOVA, Institute for the Development of New Technologies, Quinta da Torre, 2829-516, Caparica, Portugal

LIBPhys-UNL, Department of Physics, Faculdade de Ciências e Tecnologias, Universidade Nova de Lisboa,

2829-516 Caparica, Portugal

Keywords: Intelligent Chair, Pressure Sensors, Sitting Posture, Classification, Algorithmic Optimization.

Abstract: An intelligent chair prototype was developed in order to detect and correct the adoption of bad sitting

postures during long periods of time. A pneumatic system was enclosed in the chair (4 air bladders inside

the seat pad and 4 in the backrest) to classify 12 standardized sitting postures, with a classification score of

80.9%. Recently we used algorithmic optimization applied to the existing classification algorithm (based on

Neural Networks) to split users (using Classification Trees) by their sex and used two different previously

trained Neural Networks (Male and Female) to get an improved classification of 89.0% when the user was

identified and 87.1% for unidentified users. In this work we aim to investigate the usage of the

anthropometric information (height and weight) to further optimize our classification process. Here we use

four Machine Learning Techniques (Neural Networks, Support Vector Machines, Classification Trees and

Naive Bayes) to automatically split the users in 2 classes (above and below the specific anthropometric

median value). Results showed that Classification Trees worked best on automatically separating the body

characteristics (i.e. Height) with a global optimization of 88.3%. During the classification process, if the

user is identified, we skip the splitting step, and this optimization increases to 90.2%.

1 INTRODUCTION

There has been a growing interest in developing

intelligent chairs capable of detecting a person’s

sitting posture and alerting that person to improve

his or her sitting posture. Numerous researchers

applied sheets of surface-mounted pressure sensors

placed as if in a 2D array or used statistical

techniques to find the best way to place singular

force-sensitive resistors (Zhu et al., 2003; Tan et al.,

2001; Zheng and Morrell, 2010; Mutlu et al., 2007;

Daian et al., 2007; Goossens et al., 2012). Other

groups implemented sensing textiles in the chair

(Forlizzi et al., 2005). Most of these chairs alert the

users by using vibrotactile motors or by computer

pop-ups (Haller et al., n.d.; Schrempf et al., 2011;

Zheng and Morrell, 2010). Another group used 36

intelligent pneumatic actuators over sensing plates to

detect and guide the sitting posture (Faudzi et al.,

2010).

These intelligent chairs, which have shown the

capability of monitoring physiological parameters

(e.g. heart rate) (Griffiths and Saponas, 2014) or

monitor everyday activities, are starting to be

implemented in real homes for year-long tests

(Palumbo et al., 2014) and they are needed because

our society spends long periods of time in the

workplace and even at home in the sitting position.

This sedentary lifestyle has been associated with an

increased risk of cardiovascular and musculoskeletal

diseases, although some studies have not been able

to prove direct and causal correlation between sitting

time and those disorders (Chau et al., 2010;

Hartvigsen et al., 2000; Owen et al., 2010; Owen et

al., 2014; Roffey et al., 2010). Musculoskeletal

disorders were recognized as one of main causes of

work-related disability and loss of productivity in

industrialized countries (Ramdan et al., 2014;

Punnett and Wegman, 2004), so there is a necessity

for monitoring and prevention of those health

406

Martins, L., Ribeiro, B., Almeida, R., Pereira, H., Jesus, A., Quaresma, C. and Vieira, P.

Optimization of Sitting Posture Classiﬁcation based on Anthropometric Data.

DOI: 10.5220/0005790104060413

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 5: HEALTHINF, pages 406-413

ISBN: 978-989-758-170-0

dysfunctions.

When an individual is sitting, most of the

bodyweight is supported by the ischial tuberosities,

the thigh and gluteal muscles, while the rest is

transferred by the feet and armrests when they are

present (Pynt et al., 2001). During extended periods

of sitting, there is a decrease of the lumbar lordosis,

which has been implicated in increasing the physical

risk factors related to back, neck and shoulder pain

(Ariëns et al., 2001; Juul-Kristensen et al., 2004),

due to anatomical changes and degeneration of

intervertebral disks and joints, especially the lumbar

disks (Adams and Hutton, 1986; Kingma et al.,

2000; Billy et al., 2014). If a person is sitting in a so

called ‘bad posture’ (for example sitting in a leaned

back position without lumbar support, see Figure 3

for other examples), the risk of musculoskeletal

disorders increases (Lis et al., 2007)

The increase in these health disorders supports

the necessity for their monitoring and prevention,

leading to the development of chair prototypes that

identify several sitting positions and then alert or

correct the adoption of bad postures over extended

periods. Our first prototype had 4 air bladders placed

in the seat pad and 4 in the backrest, with pressure

sensors that measured the internal pressure of the

bladder. We used Artificial Neural Networks (ANN)

to classify 11 standard sitting postures, with 70%

accuracy, and we were able to do a real-time

classification of 8 postures, with 90% accuracy. This

prototype had had a rudimentary correction

algorithm based on Boolean logic (Martins et al.,

2014; Martins et al., 2013).

The second prototype was built in order to

overcome the gaps identified in the first prototype,

mainly the introduction of a vacuum pump to control

efficiently the air inside the bladders, the design of

industrially constructed air bladders and the

reorganization of the communication protocols

(Pereira et al., 2015). We then revised our

classification and correction algorithms and

introduced Fuzzy Logic to the existing ANN

algorithms, which was able to integrate time spent in

each posture (recognized by the ANN) and was able

to identify intermediate postures, other than the 12

standard ones and correct them based on fuzzy logic

actuators (Ribeiro et al., 2015). This work precedes

our previous implementation of algorithmic

optimization, applied to the second prototype in

order to improve posture classification performance,

based on the sex of the users (Ribeiro et al., 2015). It

continues the trend of classification optimization by

using the anthropometric information of the users

(height and weight) to surpass the previous

classification Accuracy, by testing various

classification methods to split the users. This study

was also driven by the discovery that our previous

classification algorithms (with leave-one-out

strategy to train with 49 users and test with the last

one) had some difficulties in the classification of

users with weights between 60 and 73 Kg and

heights between 173 and 190 cm (highlighted in the

red square in Figure 1).

Figure 1: (A) Classification Performance for each

participant regarding their Anthropometric Data (Height

and Weight), based on a Neural Net-work with 3 Layers

and 15 Neurons.

2 EXPERIMENTS AND

METHODS

2.1 Equipment – Sensors and

Pneumatic Actuators

For this work we use the second prototype that was

previously built, with 8 industrially made

polyurethane bladders, and with new features that

were improved from the first prototype, as

previously mentioned (Pereira et al., 2015). The

main objective was that the bladders covered and

distinguished the anatomical areas involved in the

weight transfer during the seated posture (Pynt et al.,

2001), such as the scapula, the ischial tuberosities,

the posterior thigh region, the lumbar spine (Martins

et al., 2014). The air bladders (see Figure 2-A for

configuration) were placed inside the original

padding foam (as can be observed in Figure 2-B, the

chair maintains the original integrity) (Pereira et al.,

2015). All the sensors and the pneumatic circuits

were integrated in eight small boxes that were

inserted in the backrest and connected to the lower

part of the seat pad, which makes all the electronics

and pneumatic circuit invisible to the user (Pereira et

Optimization of Sitting Posture Classiﬁcation based on Anthropometric Data

407

al., 2015).

Figure 2: (A) Air bladder schematic (B) External aspect of

the chair prototype.

2.2 Experimental Design - Participants

and Procedure

The same dataset is used as in the previous

optimization work (Ribeiro et al., 2015) (see Table 1

for the participants information). We split the users

based on their weight and height (see dashed lines in

Figure 1). Just as in the previous work protocol, we

use a value of 5 sec for bladder inflation and also

asked the subjects to empty their pockets and adjust

the stool to their popliteal height and to keep their

hands on their thighs (Ribeiro et al., 2015).

Table 1: Data of the participants in the experiment,

namely, Sex, Age, Weight and Height. Note:

Values for

Average±Standard Deviation and (M/F) corresponds to

(Male/Female).

Participants Information

Value

Median

Number of Subjects

(M/F)

50 (25/25) -

Age (years)

26,4±9,5 -

Weight (Kg)

66,8±12,8 67

Height (cm)

170,5±9,8 171

Our experiment consists of two tests, the first

involved showing a presentation of the postures P1

to P12 (see Figure 2), each for a duration of 20

seconds, asking the subject to mimic those postures

without leaving the chair. In the second we used the

same presentation, repeating every posture two

times, but after every 20 seconds we ask the subject

to leave the chair, take a few steps and sit back. The

twelve postures (P1 to P12 – see Figure 2) used in

this experiment represent the most common sitting

postures found in office settings (Vergara and Page,

2000; Mutlu et al., 2007; Zheng and Morrell, 2010;

Martins et al., 2014).

Figure 3: Seated postures classes: (P1) seated upright,

leaning (P2) forward (P3) back (P4) back with no lumbar

support (P5) left (P6) right (P7) right leg crossed (P8) right

leg crossed, left lean (P9) left leg crossed (P10) left leg

crossed, right lean (P11) left leg over right (P12) right leg

over left.

Not all of the 20 sec of acquisition were used (as

in previous experiments), due to the existence of a

Transition zone, where the sensor values are not

stable (Martins et al., 2014; Ribeiro et al., 2015;

Pereira et al., 2015). We extracted 100 data-points,

corresponding to 12.5 sec with a sampling of 8 Hz.

Pressure maps were done, by averaging 20

acquisitions, obtaining 5 maps so out of the 100

data-points, for a total of 9000 (50 subjects * 3

repetitions * 5 pressure maps * 12 postures)..

This (P1) pressure is used as a baseline by

subtracting its average from the entire data-points

(9000 maps). After the calibration, the maps are

normalized to an interval of [-1, 1] to use as inputs

for the Posture Classification Algorithm based on

Artificial Neural Networks (ANNs), based on the

average pressure values of the P1 posture of each

subject (Pereira et al., 2015; Martins et al., 2014). To

create ANNs we use the MATLAB® Neural

Network Toolbox™. The optimization of the

Posture Classification is based on using the baseline

pressure as an input to a Pre-Process Classification

Algorithm that is going to classify the participants

according to their anthropometric information.

HEALTHINF 2016 - 9th International Conference on Health Informatics

408

2.3 Classification Algorithms

Here we use four supervised machine learning (ML)

techniques: Artificial Neural Networks (ANNs),

Support Vector Machines (SVM), Classification

Trees (CT) and Naive Bayes (NB) to create a Pre-

Process Classification Algorithm that splits the

participants based on their anthropometric

information. These techniques are widely used in

biomedical applications (Kotsiantis, 2007; Singh et

al., 2014), are the most reliable in supervised

learning and can be easily implemented with specific

libraries (Abeel, 2009) in simple computational

architectures, such as a single-board computers (e.g.

Raspberry Pi) or Mobile Devices (smartphones or

tablets). To train and test each method we use the

MATLAB® Neural Network Toolbox™ (MNNT)

and the MATLAB® Statistics Toolbox™ (MST). To

estimate the performance of each ML technique we

used the 10-fold cross validation, using the

‘cvpartition’ function. The results are obtained by

calculating the Accuracy of the 2 class separation

problem (below and above the specific

anthropometric information), as the above the

Median can be considered the True Positive and the

Below the Median the True Negative of the test.

ANN-based algorithms have been shown to be

useful in many engineering and biomedical

applications (Paliwal and Kumar, 2009). We already

use ANNs for the Posture Classification, as they

showed the ability to handle very well that

multiclass problem. They also have an advantage of

being easily exported to mobile applications (using

the weights and bias matrices).

The Classification and Regression Trees (CART)

methods that are still being widely used in

biomedical applications (Podgorelec et al., 2002),

were first presented by Breiman and colleagues in

1984 (Breiman et al., 1984). In this work we use the

fitctree from the MST.

SVM techniques were first presented to separate

a binary class problem (Boser et al., 1992) and have

been applied to Biomedical and Biotechnology

applications, such as face recognition (Cyran et al.,

2013) or using gene expression to classify different

cancers (Noble, 2006) and classifying objects such

mass spectra (Noble, 2003; Cyran et al., 2013),

proteins (Noble, 2003), DNA sequences (Noble,

2003). Here we have also binary classification

problem, so we used the fitcsvm function, present in

the MST.

Naive Bayes is a simple and scalable technique

that has been introduced in the 1950’s and has also

been used in biomedical applications (Singh et al.,

2014). Here we use the ‘fitcnb’ function, from MST

and then changed the kernel distributions.

3 RESULTS AND DISCUSSION

3.1 Classification Optimization based

on Anthropometric Information

3.1.1 Neural Network Optimization

To search for the optimized parameters of the

Posture Classification based on Neural Networks,

we tested various combinations of layers, neurons

(as can be seen in Table 2), using the ‘tansig’

transfer function and the ‘scaled conjugate gradient

backpropagation’ (SCG) training function (using the

default parameters), which proved to be the most

accurate parameters our previous work (Martins et

al., 2014; Pereira et al., 2015). As can be seen in

Table 2, the best overall result was with 15 Neurons

and 1 Layer with an overall classification of 95.8%

(overall separation of 95.6% for Height and 96.0%

for Weight). Training with 3 Layers is not shown as

the results were lower or around 90%. It is noted that

the 1 layer-15 neurons also had the best results for

the posture classification algorithm in the first

prototype.

Table 2: Results from the Neural Network Optimization.

Number

Neurons

Class

Above

the

Median

Below

the

Median

Overall

Class

Separation

Height 97.9 93.3 95.6

Weight 97.6 94.4 96.0

Height 93.1 91.7 92.4

Weight 94.9 94.7 94.8

Height 93.9 96.7 95.3

Weight 92.8 93.9 93.4

Height 96.8 94.7 95.7

Weight 95.7 92.3 94.0

15/15

Height 94.4 92.2 93.3

Weight 96.8 93.3 95.0

20/20

Height 93.7 93.0 93.4

Weight 95.1 95.5 94.8

25/25

Height 97.0 92.4 94.7

Weight 93.9 93.5 93.7

30/30

Height 96.1 94.0 95.0

Weight 95.5 94.7 95.1

3.1.2 Classification Trees Optimization

Using the default values from the fitctree function,

we changed the splitting criterion from the Gini's

Diversity Index to the Twoing rule (Breiman et al.,

Optimization of Sitting Posture Classiﬁcation based on Anthropometric Data

409

1984) and then to the calculation of the node

deviance (Ritschard, 2006). The best score (97.8%)

were obtained with the Gini Index with an overall

separation of 97.8% for Height and 97.9% for

Weight, as seen in Table 3.

Table 3: Classification Trees Optimization results.

Splitting

Criterion

Class

Above

the

Median

Below

the

Median

Overall

Class

Separation

Gini

Height 97.6 98.1 97.8

Weight 98.9 96.8 97.9

Twoing

Height 97.1 97.6 97.3

Weight 98.4 97.9 98.1

Deviance

Height 97.3 98.9 98.1

Weight 97.1 97.3 97.2

3.1.3 Support Vector Machine Optimization

We started the SVM optimization with the default

parameters. In 'Change 1', we standardized the

predictors (using the 'Standardize' flag). In 'Change

2', we changed the 'KernelScale' to automatic, which

uses heuristic procedure to select the kernel scale

value.

In 'Change 1+2' we combined both flags, which

gave the best overall classification of 78.2% (overall

separation of 73.1% for Height and 83.2% for

Weight). In 'Change 3', we changed the 'Box

Constraint' flag to 10 and 0.1 (default is 1), along

with the flags from 'Change 1+2' (see Table 4 for all

Classification Accuracies).

Table 4: Support Vector Machine Optimization results.

Parameter

change

Class

Above

the

Median

Below

the

Median

Overall

Class

Separation

Default

Height 66.7 50.2 58.5

Weight 60.5 69.1 64,8

Change 1

Height 76.8 65.9 71.3

Weight 76.3 88.3 82.3

Change 2

Height 79.2 63.5 71.3

Weight 80.3 85.6 80.3

Change

1+2

Height 80.2 66.1 73.1

Weight 83.1 88.3 83.2

Change 3

(10)

Height 78.4 65.1 71.7

Weight 75.7 88.5 82.1

Change 3

(0.1)

Height 80.0 61.9 70.9

Weight 73.6 81.3 77.5

3.1.4 Naïve Bayes Optimization

Employing the the ‘fitcnb’ function, we started with

the default parameters, and adapted the data

distribution from 'normal' to 'kernel' with 4 possible

kernels: 'normal', 'box', 'epanechnikov' and 'triangle'.

The best results (see Table 5) was obtained with

a 'normal' kernel, with a global score of 79.8%

(78.9% for Height and 80.7% for Weight).

Table 5: Naïve Bayes Optimization results.

Parameter

change

Class

Above

the

Median

Below

the

Median

Overall

Class

Separation

Default

Height 52.3 71.7 62.0

Weight 54.1 78.4 66.3

Kernel

normal

Height 72.5 85.3 78.9

Weight 72.8 88.5 80.7

Kernel

box

Height 72.8 85.9 79.3

Weight 67.2 86.9 77.1

Kernel

epanech-

nikov

Height 73.1 84.3 78.7

Weight 66.7 86.4 76.5

Kernel

triangle

Height 72.3 81.9 77.1

Weight 65.6 84.3 74.9

3.2 Sitting Posture Classification based

on Neural Networks

After doing the class separation (above and below

the median height and weight), we now rely on

using Neural Networks to classify the 12 standard

Sitting Postures.

The chosen parameters were based on the best

results obtained in the previous experiments (Pereira

et al., 2015; Martins et al., 2014), so we also fixed

the SCG algorithm training function and ‘tansig’ for

the transfer functions and tested the Number of

Neurons (15 and 30) and the amount of Layers (1, 2

or 3).

Table 6 shows the obtained results, the best

result was with 15 Neurons and 1 Layer with an

overall classification of 90.0% (overall separation of

90.2% for height and 89.8% for weight).

This simpler configuration is also advantageous

to use, especially in real-time classification, to avoid

the overfitting problem (Martins et al., 2014).

Overtraining of the Algorithms was avoided by

using the ‘cvpartition’ (with 10-fold option), which

then test’s with 10% of the data and trains with 90%,

and repeats this process 10 times and averages the

results. Although there are a lot of parameters that

could have been used for each of the previous

machine learning algorithm, as we expressed in the

previous sections, we wanted to use a simple

approach to the classification process, because we

want to export the Algorithms to a small single-

board computer (e.g. Raspberry Pi) or to a mobile

application.

HEALTHINF 2016 - 9th International Conference on Health Informatics

410

Table 6: Results for Posture Classification based on

Neural Networks.

Number

Neurons

Class

Above

the

Median

Below

the

Median

Overall

Separation

Height 90.5% 89.9% 90.2%

Weight 89.8% 89.7% 89.8%

Height 87.9% 88.3% 88.1%

Weight 86.9% 87.3% 87.1%

15/15

Height 89.5% 87.2% 88.4%

Weight 87.9% 90.0% 89.0%

30/30

Height 89.8% 86.4% 88.1%

Weight 87.7% 90.6% 89.2%

15/15/15

Height 89.0% 90.4% 89.7%

Weight 88.2% 89.2% 88.7%

30/30/30

Height 89.2% 88.6% 88.9%

Weight 87.8% 89.3% 88.5%

4 CONCLUSIONS AND FUTURE

WORK

In prior works, we developed two intelligent sensing

chair prototypes. The first one was developed to

classify 11 standardized sitting postures using 8

pneumatic bladders connected to pressure sensors

(Martins et al., 2014). The second solved the

identified limitations of the first one (using a

vacuum pump to control the deflation of the

bladders, the design of industrially built bladders

and the use of simple computational architectures)

and had a classification score of 80.9% of 12

standard sitting postures . This work aimed to

demonstrate how we could optimize this

classification based on the identification of the user,

and split them by their anthropometric information

(above or below the median height and weight), with

each class having their specific ANN for Posture

classification.

The workflow of the classification optimization

process is shown in Figure 4. This process starts

with the user sitting on the chair prototype and the

pressure sensor acquisition. If the user is identified

in the computer interface, we just directly select the

specific Neural Network for Posture Classification,

based on the anthropometric features. If the user is

not identified, we need to detect which Neural

Network should be used, by using the best Pre-

Process Algorithm. The workflow then continues

with the Calibration and Data processing, finalizing

with the Posture Classification Process based on the

specific ANN.

The Best Pre-Process Algorithm (Classification

Trees with the Gini Index) for our specific problem,

gave an automatic separation score (97.8%), with an

overall separation of 97.8% for Height and 97.9%

for Weight.

Results showed that the best result for the

Posture Classification (using the ANN) was obtained

with Layer of 15 Neurons with an overall

classification of 90.2% for height and 89.8% for

weight, which translates into an overall optimization

of 9.3% (with the height) from the previously

reported result of 80.9% score for 12 standard sitting

postures (Pereira et al., 2015) and a 1.2% increase

over the previous optimization (using the sex of the

user) (Ribeiro et al., 2015) when the user is

identified with their anthropometric information.

Combining the automatic separation (when the

user is not identified), we use a pre-process

classification (based on Decision Trees) to

determine the specific Anthropometric Neural

Network, so by multiplying each specific result we

get an overall classification optimization of 88.3%

for the Height and 87.8% for the Weight, resulting in

an overall optimization of 7.4% over the normal

Posture Classification Algorithm and an increase of

1.2% over the previous optimization process.

Although using the Height optimization gave the

best results, we believe that combining all three

factors (Height, Weight and Sex) into a very

personalized Classification Algorithm will be our

best option to get scores higher than 90% and

optimize the sitting posture process, which will only

be achieved by increasing the participant’s database.

Figure 4: Workflow of the Posture Classification

Optimization Process.

The prototype is still undergoing a series of

operational trials in an office environment to

evaluate the classification algorithms to get realistic

statistical data from of daily postural habits. The

correct classification of different sitting postures is

necessary for the implementation of the posture

Optimization of Sitting Posture Classiﬁcation based on Anthropometric Data

411

correction algorithms that hopefully will have a

societal impact of reducing the common back and

neck disorders.

ACKNOWLEDGEMENTS

This project (QREN 13330 – SYPEC) is supported

by FEDER, QREN – Quadro de Referência

Estratégico Nacional, Portugal 07/13 and

PORLisboa – Programa Operacional Regional de

Lisboa. The authors wish to thank Eng. Pedro

Duque, Eng. Rui Lucena, Eng. João Belo and Eng.

Marcelo Santos for the help provided in the

construction of the first prototype.

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