System for Posture Evaluation and Correction

Development of a Second Prototype for an Intelligent Chair

Hugo Pereira

, Leonardo Martins

1,2

, Rui Almeida

, Bruno Ribeiro

, Cláudia

Quaresma

1,3

Adelaide Ferreira

and Pedro Vieira

1,4

Department of Physics, Faculty of Sciences and Technology New University of Lisbon,

Quinta da Torre, 2829-516, Caparica, Portugal

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

CEFITEC, Faculty of Sciences and Technology New University of Lisbon, Quinta da Torre, 2829-516, Caparica, Portugal

Centre of Atomic Physics, Faculty of Sciences and Technology New University of Lisbon,

Quinta da Torre, 2829-516, Caparica,, Portugal

Keywords: Posture Classification, Posture Guidance, Pressure Distribution, Sensing Chair, Sitting Posture.

Abstract: The sitting position has become one of the most common postures in developed countries. However,

assuming a poor sitting posture leads to several health problems, namely back, shoulder and neck pain. In a

previous work, an intelligent chair was developed and was shown to classify and correct the seating

position. This work describes improvements on this intelligent chair prototype culminating with the

development of a new prototype. The improvements of this new prototype are presented, resulting in new

studies for posture identification. Pressure maps for 12 sitting postures were gathered in order to

automatically detect user’s posture through a neural network algorithm, obtaining an overall posture

classification of around 81%.

1 INTRODUCTION

Nowadays, due to the rapid technological

development, automation and computerization of the

workplace, sitting has become the most common

posture in developed countries (Maria et al. 2007;

Chau et al. 2010; Hartvigsen et al. 2000; Graf et al.

1995). When a sitting position is adopted, most of

the bodyweight is supported by the ischial

tuberosities, thigh and the gluteal muscles. The rest

of the weight is distributed to the ground through the

feet and to the backrest and armrest when they are

available (Pynt et al. 2001). Furthermore, assuming

a poor posture can lead to back and neck pain due to

the anatomical changes of the spine and the

degeneration of the intervertebral discs and joints

(Lis et al. 2007; Graf et al. 1995). This fact has a

huge impact in the cost of work-related illness

(Waters 2004). Good posture is defined as the state

of balance between musculoskeletal structures that

prevents the appearance of lesions or their

progressive deformation, and its adoption should

prevent compensatory movements and evenly

distribute the weight (Pynt et al. 2001).

Through the years, several investigations have been

made to solve the problem of incorrect sitting

posture. Most of these researches are focused on

automatically detecting and classifying of the test

subject based on pressure maps given by pressure

sensors placed in chairs. Tan et al. (2001) used a

chair as an interface for human-computer

interactions. They used two Tekscan® sensor sheets,

with 42-by-48 sensing units for real time capturing

of contact information between the chair and its

occupant in both seat pad and backrest. The use of

pattern recognition technics to develop a static

posture classification algorithm, such Principal

Component Analyses (PCA), achieved an overall

classification of 96% and 79% for familiar and

unfamiliar users, respectively. Using the same

sensing system, Mota & Picard (2003) managed to

classify 9 static postures in real time, achieving an

accuracy of 87.9% when tested with postures from

new subjects. Zhu et al. (2003) compared several

classification algorithms regarding their static

posture classification capabilities and found that

PCA and Sliced Inverse Regression (SIR)

outperformed, in terms of overall posture detection,

204

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

System for Posture Evaluation and Correction - Development of a Second Prototype for an Intelligent Chair.

DOI: 10.5220/0005286002040209

In Proceedings of the International Conference on Biomedical Electronics and Devices (BIODEVICES-2015), pages 204-209

ISBN: 978-989-758-071-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

the k-Nearest Neighbour and Linear Discriminant

Analysis.

Andreoni et al. (2002) combined a motion

capture optoelectronic system and suitable pressure

sensor matrices to measure a car driver’s posture

parameters.

With the aim of reducing the number of pressure

sensors for posture identification Mutlu et al. (2007)

and Zheng & Morrell (2010) made another

approach. The first group studied a way to reduce

the number of sensors to 19, obtaining an overall

classification accuracy of 78%, improving the

classification when the number of sensors was

increased to 31, to 87%. The second group

developed a system with only 7 sensors and 6

vibrotactile actuators, designed to posture guidance

through haptic feedback. With a classification

algorithm based on the mean squared error between

the pressure measurements and the reference

pressure for each static posture, an overall accuracy

of 86.4% was achieved when distinguishing among

10 postures. This study also showed that is possible

posture guidance trough haptic feedback.

Daian et al. (2007) developed a simple system

where they used a chair equipped with force sensors,

one in the seat pad and other in the backrest. The

first one detects if someone is sitting and the other

informs about the adequateness of the sitting

position. The feedback to the subject, regarding

posture and time, is given trough a computer

program that identifies 3 different situations: the

person is not sitting, the person is sitting in an

adequate position, and the person is sitting in an

inadequate position. These 3 situations were

determined comparing the pressure values received

from the sensor with preset threshold values. The

threshold values were determined through repeated

measurements values in the same way that the

sensors' positions on the chair were determined. If an

inadequate posture was taken for more than 20

seconds or the user was sitting for more than a 20,

40 and 60 minutes a feedback to the user is given.

The feedback to the user is provided by a warning

agent placed on the desk next to the computer

display. Its warning should be understood as the

need to do a break.

Other approaches that do not need the use of

classification algorithms and interfaces with pressure

sensors can be taking in consideration. One of that’s

approaches can be the use of software like

WorkPace (Blangsted et al. 2004). This kind of

software is intended to educate users about muscle

fatigue and recovery. It recommends regular

exercises and stretching, displays alerts when breaks

are recommended, monitors the exposure and

intensity of computer use, and provides feedback.

In previous works (Martins et al. 2014; Lucena

et al. 2012), a chair prototype was built with the

objective of correcting and preventing poor posture.

In this first prototype the pressure cell concept was

introduced and its capabilities of differentiating 11

different posture using 8 air bladders distributed in a

matrix of 2 by 2 in the backrest and in the seat pad.

These air bladders were able to obtain pressure maps

and change their conformation (the amount of air

inside the bladders) by inflation and deflation. Our

main hypothesis is that by increasing discomfort

when a poor posture is adopted, the user will be

encouraged to change his position. That discomfort

will be made by inflating and deflating the air

bladders. We can also induce changes in the chair

conformation over a period of time, which can help

to eventually distribute the applied pressure on

contact zones, reducing user fatigue and discomfort

due to the pressure relief on compressed tissues. In

order to classify 11 different postures, the pressure

maps were used as input for an Artificial Neural

Network (ANN). The ANN were exported to a

mobile application and have been able to execute

postural classification in real-time. Results show

that, for 11 postures, in real-time classification the

overall score was 70%, but when the number of

positions decreases to 8, the overall classification

score was 93%. Two correction algorithms were

integrated in the mobile application in order to test if

the user is seated for long periods of time and also if

he or she is seated in an incorrect posture. In both

situations the chair’s conformation automatically

changes, inducing the user to adopt a more correct

posture. Despite significant achievements of this

previous work, some improvements are needed to

develop a better intelligent chair capable of real-time

classification and correction of sitting posture. For

that reason, the aim of this paper is present a new

prototype with improved features.

2 EQUIPMENT DEVELOPMENT

As in Martins et al. (2014) work, the aim of this

project was to adapt a regular office chair for sitting

posture detection and correction. Taking into

account the limitations in the previous prototype, we

propose to build a new improved one. With this

intent, several changes were made regarding the

design and control of the air bladders.

In order to posture guidance and correction, is

required an interface capable of measure the applied

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pressure and change the chair´s conformation. For

that purpose air bladders were developed that are

able to inflate and deflate. One of the limitations

existing in the old prototype was the fact that the air

bladders were made manually using off the shelf

materials such as water bags used in back packs. The

problem with this approach lies in the fact that the

air bladders were not all identical, creating a

problem at the time of system calibration and

control. Therefore, for the new prototype, new air

bladders were design. The new air bladders were

design by NGNS Ingenious Solutions and

industrially built by Aero Tec Laboratories Inc. This

way we can guarantee all pressure cells have the

same dimensions and correctly fit the chair. In

Figure 1 it is possible to see the design of the air

bladders and how each one is distributed into a

matrix of 2 by 2.

Figure 1: On the left is shown the design of the new air

bladders and on the right their position on the chair.

Two matrices containing the bladders were

placed under the original padding foam of the chair

in order to maintain the anatomical cut of the seat

pad and the back rest, totaling 8 air bladders. While

building the previous prototype, the calculated

pressure inside the bladders were shown to not be

significantly altered by using the original padding

foam of the chair.

Air bladders placement was strategically chosen.

According to previous literature, there are two types

of approaches: a pure mathematical and statistical

approach (Mutlu et al. 2007) and an anatomical

approach (Zheng & Morrell 2010). Based on the

second method we placed the air bladders in order to

cover the most important and distinguishable areas

of the body for detecting a seated posture, such as

the ischial tuberosities, the posterior thigh region,

the lumbar region of the spine and the scapula.

These are also the areas where most of the

bodyweight is distributed (Pynt et al. 2001).

In the previous prototype, all instrumentation

responsible for the control and measure of pressure

in the bladders was centralized in two places, one for

the backrest and other to the seat pad. In this new

prototype we approach the problem in a modular

way. Each one of the eight bladders is connected

through a rubber pipe to its control module. In case

of malfunction in a control module, it can easily be

replaced without compromising the others.

Figure 2: A – Placement of the control modules in the

back rest; B – Placement of the control modules in the seat

pad.

The control module is, in itself, an improvement.

Previously, each bladder would be connected to an

air pump and a solenoid valve. Air would flow out

of the bladders when the valve would open and

through gravity’s action, meaning there was no

effective control when decreasing pressure. Since

the system’s goal is to correct the user’s sitting

posture, it was understood that it would be an asset

to have a better control over the amount of air that

leaves the air bladders. For that reason, a vacuum

pump was added to each control module. In each

control module we can also find a piezoelectric

gauge pressure sensor to measure the internal

pressure of the bladder. The piezoelectric gauge

pressure sensors used were the Honeywell 26PC

Series rated to 5 psi with a sensitive of 10 mV/psi.

The placement of the control modules in the chair

can be seen in Figure 2.

In Martins et al. (2014), control and processing

of the pressure maps from the chair was essentially

made by the use of a smartphone. To achieve

independence from the use of a smartphone, a

single-board computer, the Raspberry Pi, was used

to control the chair’s instrumentation through an I

connection. A Bluetooth interface was added to the

RPi in order to connect it to the outside world,

enabling one to retrieve data and statistics through a

smartphone.

3 CLASSIFICATION OF SITTING

POSTURE

To create a Seated Posture Classification Algorithm,

one experiment for data acquisition was conducted

with the dataset presented in Table 1.

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Figure 3: Seated postures used in the experiments and their respective class label: (P1) seated upright, (P2) leaning forward,

(P3) leaning back, (P4) leaning back with no lumbar support, (P5) leaning left, (P6) leaning right, (P7) right leg crossed,

(P8) right leg crossed, leaning left, (P9) left leg crossed, (P10) left leg crossed, leaning right, (P11) left leg over right, (P12)

right leg over left.

Before the experiment starts, it was necessary to

inflate the air bladders in order for them to have

enough air to accurately obtain the pressures maps.

It is important to say that the time of inflation must

be carefully chosen because is essential that the air

inside the air bladders doesn’t be enough to cause

discomfort to the users. For that reason, after some

tests, we decided to use a value of 5 seconds for

inflation. Before undergoing any experiment,

subjects were asked to empty their pockets and to

adjust the stool height so that the knee angle (angle

between the thigh and the leg) was at 90º and to

keep their hands on their thighs.

The experiment was comprised of two tests, the

first test involved showing a presentation of the

postures from P1 to P12 (for a duration of 20

seconds each), asking the subject to mimic those

postures without leaving the chair.

The second consisted in showing the same

presentation, with every posture being repeated two

times, but after every 20 seconds we asked the

subject to walk out of the chair, take a few steps and

sit back. The twelve postures chosen were based on

previous works and are represented in Figure 3 (Zhu

et al. 2003; Forlizzi et al. 2005; Tan et al. 2001;

Zheng & Morrell 2010; Mutlu et al. 2007; Vergara

& Page 2000).

Not all of the data acquired was used for the

classification, because when a user changes his

posture, the pressure maps will oscillate (Transient

zone) until they stabilize (Stable zone) as shown in

Figure 4. Here, we focus our study on the Stable

zone of the pressure maps and therefore, 12.5 out of

the 20 seconds were used. Since our sampling rate is

8 Hz, we were able to extract 100 data-points out of

the 12.5 seconds, which were divided in groups of

20 points. The average of those groups was used to

create 5 pressure maps for posture classification,

giving a total of 1080 maps for each posture (72

subjects x 3 repetitions x 5 pressures maps) and a

total of 12960 maps (1080 x 12 postures). All the

12960 maps were normalized to an input interval of

[-1, 1] for the ANNs. For the creation of the ANNs

we used the MATLAB® Neural Network

Toolbox™.

Table 1: Data of the participants in the experiment,

namely, Sex, Age, Weight and Height. Note: a Values for

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

(Male/Female).

No. of

subjects (M/F)

Age

(years)

Weight

(Kg)

Height

(cm)

72 (37/35) 26,6±9,3 67,7±12,7 170,8±9,4

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Figure 4: Pressure measurement from one air bladder,

when a subject went from posture P7 to P8, showing the

transient zone and the stable zone.

4 RESULTS AND DISCUSSION

4.1 Improvements of the Prototype

With the new prototype ready, it is possible to

compare it with its predecessor. Looking at Figure 5,

it can be seen that the changes made to the chair’s

structure were virtually inexistent when compared to

the previous model. In the previous one, great

number of important adaptions was required in order

to accommodate all the necessary components for

the functioning of the classification system. This

implies that this new prototype is closer to being an

easily adaptable system, regardless of the used chair.

The introduction of a vacuum pump in the

pneumatic module greatly improved the correction

control, since, as it was already mentioned, it is now

possible to effectively monitor the amount of air that

leaves the bladders.

4.2 Seated Posture Classification

Algorithm

For the parameterization of the ANN we used a

combination of 1 layer, 40 neurons and resilient

back propagation algorithm as the network training

function. We trained a new ANN to gather the

weights and bias in order to export them for real

time posture classification. For this we divided the

entire dataset in 60% for the ANN training, 15% for

the validation and the rest for the ANN testing. The

confusion matrix for all data is represented in Table

2 with the respective overall scores for each posture

class and the overall classification score. We

obtained an overall classification of 80.9%. This

result is comparable with the overall classification in

the work done by Martins et al. (2014). It is

important to notice that for this new test two new

positions were added, the P11 and the P12. As

expected postures with lateral inclination (P5, P6, P8

and P9) weren’t well distinguish between them. The

same problem is verified in positions with the legs

cross. To solve this situation, in a parallel work, we

are developing other classification alghoritms to

combine icnclination and leg crossing positions.

In the previous prototype (Martins et al. 2014), a

problem with the classification of posture P1 was

identified. The solution to solve that problem was to

use decision trees with specific threshold values to

divide into two neural networks (one specialized

Table 2: Confusion Matrix for posture classification of the training data, where rows indicate the Output Class and columns

indicates the Target Class. The Target Class labels correspond to the respective postures from figure 3. The grey boxes in

the main diagonal give the output classes that were correctly classified as the target class. The row and column in grey give

the percentages of correct classification in relation to the respective class and the overall classification score.

P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 (%)

Output Class

P1 911 7 54 4 1 4 6 2 30 5 0 4 88.6

P2 19 989 11 6 21 8 31 9 8 9 8 7 87.8

P3 27 9 815 42 0 11 102 4 41 16 0 1 76.3

P4 0 3 26 957 13 0 5 9 21 5 19 28 88.1

P5 0 6 5 1 860 1 33 5 1 137 16 0 80.8

P6 0 0 0 1 5 815 5 115 27 5 0 30 81.3

P7 19 0 63 0 14 7 671 27 22 16 17 27 76.0

P8 0 0 2 2 0 102 26 777 7 0 3 54 79.9

P9 44 1 13 1 2 31 14 20 722 58 30 45 73.6

P10 0 1 24 4 86 0 11 0 35 721 23 7 79.1

P11 0 1 1 6 23 0 71 14 22 46 900 10 82.3

P12 5 8 11 1 0 46 50 43 89 7 9 812 75.1

(%) 88.9 96.5 79.5 93.4 83.9 79.5 65.5 75.8 70.4 70.3 87.8 79.2

80.9

Target Class

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Figure 5: Representation of the old (A) and new (B)

prototype.

identifying posture P1 and the other to classify the

remaining postures). However, preleminary tests

show that this new prototype will not need the

division in two ANNs due to its higher structural

stability, which caused classification problems in the

previous prototype. In the future, real-time

classification tests must be conducted to evaluate the

accuaracy of this new Prototype.

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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