Sitting Posture Detection using Fuzzy Logic

Development of a Neuro-fuzzy Algorithm to Classify Postural Transitions

in a Sitting Posture

Bruno Ribeiro

, Leonardo Martins

1,2

, Hugo Pereira

, Rui Almeida

, Cláudia Quaresma

1,3

Adelaide Ferreira

and Pedro Vieira

1,4

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

Quinta da Torre, 2829-516, Caparica, Portugal

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

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

Caparica, Portugal

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

Quinta da Torre, 2829-516, Caparica, Portugal

Keywords: Intelligent Chair, Pressure-distribution Sensors, Sitting Posture, Posture Classification, Fuzzy Logic, Neural

Networks.

Abstract: In a previous work, a chair prototype was used to detect 11 standardized seating postures of users, using just

8 air bladders (4 in the chair’s seat and 4 in the backrest) and one pressure sensor for each bladder. In this

paper we describe a new classification algorithm, which was developed in order to classify the postures

using as input the Centre of Pressure, the Posture Adoption Time and the Posture Output from the existing

Neural Network Algorithm. This new Posture Classification Algorithm is based on Fuzzy Logic and is able

to determine if the user is adopting a good or a bad posture for specific time periods. The newly developed

Classification Algorithms will prompt the improvement of new Posture Correction Algorithms based on

Fuzzy Actuators.

1 INTRODUCTION

Society sedentary behaviours are influenced by

many factors and on various domains, including

spending extended periods of time in a sitting

position in a variety of settings, such as the

occupational workspace, transports, leisure activities

and household activities (Owen et al., 2011; Chau et

al., 2010).

Low back pain has been identified as one of the

leading causes of work-related disability and loss of

productivity in industrialized countries (Ramdan et

al., 2014; Punnett and Wegman, 2004), but

systematic studies haven’t been able to establish a

causal and independent relationship between

occupational sitting and low back pain (Hartvigsen

et al., 2000; Roffey et al., 2010), although an

increasing risk of this disorder has been associated

with sitting during extended periods of time (Todd et

al., 2007).

This situation happens because when a person

has prolonged sitting behaviours, this will lead to a

decrease of the lumbar lordosis (Van Dieën et al.,

2001; Cagnie et al., 2007) which then increase the

physical risk factors related to back, neck and

shoulder pain (Ariëns et al., 2001; Juul-Kristensen et

al., 2004). This pain is due to anatomical alterations

and degeneration of the intervertebral disks and

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

2000).

Recent studies have shown that still doesn’t exist

a consensus on what comprises a neutral spine

posture in a sitting posture (O’Sullivan et al., 2012)

due to the difficulty of doing quantitative clinical

studies that target the identification of 'correct' and

'incorrect' postures. The main approaches of such

studies is to use multiple camera sensors to build a

3D optical model of the body posture (Edmondston

et al., 2007) or using sensors applied directly to the

skin able to assess the spinal angles in different

postures (Claus et al., 2009) or to calculate the

relative distance between anatomical landmarks of

191

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

Sitting Posture Detection using Fuzzy Logic - Development of a Neuro-fuzzy Algorithm to Classify Postural Transitions in a Sitting Posture.

DOI: 10.5220/0005287101910199

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pages 191-199

ISBN: 978-989-758-068-0

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

the spine (O’Sullivan et al., 2010).

In a normal sitting position, the ischial

tuberosities, the thigh and gluteal muscles support

most of the person’s weight, while the remaining

pressure load is transferred to the ground by the feet

and to the backrest and armrests (Pynt et al., 2001).

In most of the ‘incorrect’ postures the trunk is not

totally supported in the backrest due to the lateral

flexion of the upper part of the trunk, or due to

forward or backward inclination of the trunk or due

to leg crossing. In these positions, the weight is

incorrectly transferred which can have an amplifying

effect in back and neck associated disorders (Lis et

al., 2007).

To promote a more dynamic sitting posture and

increase physical activities, chair manufacturers

have developed office chairs with structural

elements such as, seat pan motors, seat pan

suspension systems and moveable joints that can

permit movement in the horizontal plane or even

freely in all directions. Studies showed that different

chairs didn’t influenced a significant difference body

dynamics (such as muscle activation), while each of

the studied task strongly affected the body dynamics

(Ellegast et al., 2012). This suggests the need to

develop a intelligent chairs capable of identifying

the user posture or the associated task, and alerting

the users of prolonged sitting behaviours.

Such intelligent chairs have been developed in

recent years by numerous research groups, by

implementing sheets of surface-mounted pressure

sensors placed in a 2D array or using pure

mathematical and statistical approaches to find the

best way to place singular force-sensitive resistors in

the chair or even conductive textiles (Zhu et al.,

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

and Morrell, 2010; Schrempf et al., 2011; Mutlu et

al., 2007).

These intelligent chairs have been shown to be

capable of detecting the presence of a person, detect

the sitting posture of that person and alerting the

person to improve their sitting posture or even as an

input device in an office environment. They can be

used as physiological monitor and create report tools

of everyday activities which is already being

implemented in real homes for year-long tests

(Palumbo et al., 2014).

In a previous work, a chair prototype was used to

classify 11 standardized seating postures of users,

using 8 air bladders (4 in the chair’s seat and 4 in the

backrest) by using pressure sensor for each bladder

to detect the bladder’s interior pressure (Martins et

al., 2014). The bladders from the previous prototype

are shown in Figure 1-A. The classification

algorithm was based on Neural Networks and

Decision trees and was able to make a real-time

overall classification 93% (for eight postures) and

dropped to 70% (for the eleven standardized

postures) (Martins et al., 2014).

Figure 1: (A) Interior of the previous prototype and Centre of Pressure (COP) measurement (B) New Prototype (C)

Schematic of the new air bladder system and COP measurement. Bladder 1 (Back Left – BL), Bladder 2 (Back Right – BR),

Bladder 3 (Front Left - FL), Bladder 4 (Front Right - FR), Bladder 5 (Up Left – UL), Bladder 6 (Up Right – UR), Bladder 7

(Down Left – DL) and Bladder 8 (Down Right – DR).

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In this work we integrate Fuzzy Logic to the

existing Neural Network-based Algorithm to classify

the transition between postures and intermediate

postures that cannot be accurately classified using

the previously developed algorithms.

2 EXPERIMENTAL SECTION

2.1 Equipment

In a previous work, a chair prototype was built with

a 2-by-2 matrix of air bladders (see Figure 1-A).

Each individual air bladder was made with

thermoplastic polyurethane, were manually sealed

and had 20x19cm of dimensions and were placed in

the seat pan and the backrest (as can be seen in

figure 1). This arrangement cover the most

important and distinguishable areas of the seated

posture (Pynt et al., 2001), such as the ischial

tuberosities, the posterior thigh region, the lumbar

region of the spine and the scapula (Martins et al.,

2014).

For this project we started by projecting a new

set of air bladders that were industrially made (this

makes a big difference as it is guaranteed that the

volume inside each bladder is the same when they

are fully inflated) and integrated in a different office

chair (seen in Figure 1-B). The new bladder system

and its measurements can be seen in figure 1-C. The

bladder placement had the same strategy of the

previous work. The original padding foam was also

used, and placed above the pressure bladders, in

order to keep the original anatomical cut of the seat

pad and backrest. As it is guaranteed that each

bladder has exactly the same geometry, in this case

we also used the same pressure sensors for the

backrest and seatpad (in the previous work, the

sensors were different), which were the Honeywell

24PC Series piezoelectric gauge pressure sensor,

with a max rate of 15 psi, with a sensitivity of

15mV/psi. The values of x

, x

, z

correspond to

around 10.5 cm and y

, y

to around 8.5 cm.

This values is calculated by halving the bladders

sizes (20.5 and 16.5), adding half of the 10 mm

spacing between each bladder, and make rounding

adjustments due to the curvature of the bladders.

Bluetooth communication was also added to this

prototype, as in the previous prototype, making it

capable of transferring the daily postural information

to computers and smartphones, allowing a statistical

analysis of the postures taken during the day

(Martins et al., 2014).

2.2 Experimental Procedure and

Participants

Two experiments were done for this work. The first

one (A) followed a similar protocol then in the

previous work (Martins et al., 2014) and served for

data acquisition in order to create the Seated Posture

Classification Algorithms, based on Neural

Networks. The second experiment (B) was used to

study how the Neural Network behaves during

standard posture changes (in intermediate postures).

From the previous experiments (Martins et al.,

2014) we increased the number of subjects from 30

to 50, and also tried to have more indicative sample

of office workers (increasing the Age of the

participants from around 21 to 26). The dataset for

both experiments is presented in Table 1. Based on

the knowledge from previous experiments (Martins

et al., 2014) and since now the bladders have exactly

the same volume, we inflated all the bladders for 5

seconds (instead of having different inflation times

for each bladder) so we could take precise reading of

the bladder interior pressure, but not enough to

originate discomfort to the users. Before the

experiment, the participants were asked to empty

their pockets and to adjust the stool height to the

popliteal height.

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)

Experiment A

50 (25/25) 26,4±9,9 66,8±12,1 170,9±10,0

Experiment B

12 (6/6) 25,8±6,6 72,8±12,1 173,1±10,7

For the first experiment, we used a similar protocol

then the previous work (Martins et al., 2014), and

showed a presentation of postures P1 to P6 (as can

be seen in figure 2), each for a duration of 20

seconds. First we asked the participants to mimic the

postures without leaving the chair. Then we asked

them to repeat the same postures two more times,

but after every postural change we asked the

participant to walk out of the chair and move to a

certain point in the room and sit back again. The

chosen postures were the same as the previous work

(Martins et al., 2014), which were based on the most

familiar postures observed in office environments

(Zhu et al., 2003; Forlizzi et al., 2005; Tan et al.,

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

Vergara and Page, 2000)

SittingPostureDetectionusingFuzzyLogic-DevelopmentofaNeuro-fuzzyAlgorithmtoClassifyPosturalTransitionsin

aSittingPosture

193

Figure 2: Seated postures used in the experiments and

respective class label: (P1) seated upright, (P2) leaning

forward, (P3) leaning back, (P4) leaning back with no

lumbar support, (P5) leaning right, (P6) leaning left.

As in previous work, on experiment A we also

didn’t use the entire 20 seconds of acquired data,

because when a user changes its posture, the sensed

pressure values oscillate (Transition zone) and then

stabilize (Stable zone) as shown in figure 3. The

chosen data to be used as input was extracted only

from the Stable zone.

Here, using a sampling rate of 8 Hz (which is

enough to classify sitting posture behaviour), we

took 100 time-points (which correspond to 12.5

seconds out of 20 for each posture), and divided

them in groups of 20 pressure acquisition. Each

group was averaged, forming 5 pressure maps that

serve as input to the Neural Networks, contributing

to a total of 4500 pressure maps (50 participants * 6

postures * 5 averages * 3 repetitions).

For experiment B, we wanted to see how the

Neural Network algorithm worked during the

Transition zone (see figure 4) between Posture P1

and other Postures (which for this first experiment is

only done to P5 and P6). For this, we asked the

participants to move from P1 to P5, back to P1, then

move to P6 and back to P1 (and repeat this 5 times).

We observed that during a postural transition, the

classification was intermittent (this corresponds to

the Transition Zone identified in Figure 4). So for

this experiment we took a picture when the ANN

algorithm first changed its value (entering the

Transition Zone), and a second picture when the

algorithm kept giving the right answer (entering the

Stable Zone) The observed results from the

influence of the lateral angle in the Neural Network,

prompted the integration of Fuzzy Logic to cope

with the instability of the Neural Network.

2.3 Classification Methods

Artificial neural network-based classification

algorithms have been shown to be useful in many

engineering and biomedical applications (Paliwal

and Kumar, 2009). In the previous work we have

used them and since they show the ability to handle

very well our multiclass problem, we took the

decision to continue using them. They also have the

advantage of being easily implemented in other

systems (by importing the weights and bias

matrices).

For the parameterization of the ANN, we tested

again the same parameters as in the previous work,

by making combinations of layers, neurons, training

and transfer functions (Martins et al., 2014).

Fuzzy logic and specially Neuro-fuzzy

Figure 3: Confusion matrices for the training data (left matrix - corresponding to 60% of the total data), validation data

(centre matrix – 15 % of data), and testing data (left matrix – 25% of data).

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integrations have also been used in many

engineering and biomedical applications (Kar et al.,

2014). Here we use as input variables for the Neuro-

fuzzy algorithm, the Centre of Pressure (COP) of the

seat pad and backrest (which are calculated based on

equations 1-4 and figure 1), the posture output from

the Neural Network and the time spent in that output

posture. This algorithm evaluates the posture each

15 minutes.

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3 RESULTS AND DISCUSSION

3.1 Results from Experiment A

As in the previous study, the best result was again

using the Resilient-back Propagation algorithm, with

the tansig function, but now with 1 layers of 40

neurons (comparing with the 1 layer of 15 neurons

from the previous work). This result shows that our

new bladder system changed slightly the pressure

maps, which influenced how the classification

algorithms adapted. The confusion matrix for the

training, validation and testing data, using the best

parameters is shown in figure 3.

3.2 Results from Experiment B

For experiment B, first we studied how the lateral

angle influenced the classification algorithm. Figure

4 shows one cycle of this experiment, where it

shows the Transition Zones between postures. To

calculate the angle where these transitions occur, we

took a picture of the participants every time they

entered into a new Zone, and then measured the

trunk inclination in Position P1 (for reference) and

the trunk inclination in the other positions. In order

to include the variability among raters this

measurements were done by 3 different experts, so

we also calculated the degree of reliability among

raters by using two way Intra Class Correlation

(ICC) for each Angle measurement (Fleiss., 1986a).

The average, standard deviation values of each

Transition Angle and the ICC score are presented in

Table 2. For the first Transition, we identified an

average angle of 11.0º (for P5) and 13.5º (for P6),

and for the second Transition we identified an angle

of 17.6º and 20.0º, respectively for P5 and P6. This

Figure 4: Example the pressure map for one cycle of Experiment B. The transition from a Stable Zone in P1 to a Stable

Zone in P6 (in green arrows) and then P1 to P5 goes through a Transition Zone (in red arrow), where the classification is

intermittent. Legend of each bladder is according to figure 1.

SittingPostureDetectionusingFuzzyLogic-DevelopmentofaNeuro-fuzzyAlgorithmtoClassifyPosturalTransitionsin

aSittingPosture

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values are in good agreement with the identification

of a lateral trunk bending angle, which has normally

been defined at 15º (Hobson, 1992). This angle is in

our Transition Zone, where we are able to classify as

a lateral bending posture (P5 or P6), but there are

also mistakes with other postures, due to shear

movement (P3 and P1). The ICC values show that

the angles measured by each expert rater have good

reliability (values between 0.5 and 0.75 have a good

reliability, and higher than 0.75 have an excellent

reliability), validating our measurements (Fleiss.,

1986b).

We use a two-sample Kolmogorov-Smirnov

(KS) test, to check if the P5 and P6 angles have of

each subject have the same distribution. Table 2

shows the results of the KS tests for each Transition

and for each subject at a 1% significance level.

Table 2: Measurement of the lateral angle when the

Transition Zone if identified from P1 to the respective

Posture, and when the angle when the Stable Zone is

identified of the respective Posture.

Subject

1st – P5

(/º)

2nd – P5

(/º)

1st – P6

(/º)

2nd – P6

(/º)

10.7±2.2

17.5±2.6

12.9±3.4 20.6±2.4

6.5±0.7

11.8±1.1

6.7±1.2 11.4±1.0

13.5±2.4

19.6±2.7

16.7±3.6 23.9±2.2

8.4±1.4

15.1±1.9

9.2±1.1 14.0±1.6

11.6±3.4

22.2±2.9

13.9±3.0 25.7±6.0

12.9±2.2

21.4±2.5

17.3±3.5 22.9±5.0

16.2±3.6

23.2±2.5

26.7±4.3 35.5±3.3

11.3±1.4

17.2±1.6

10.4±1.5 16.8±2.4

7.4±1.7

12.9±2.7

10.4±1.6 15.2±1.8

11.5±1.4

17.3±2.3

11.5±1.4 17.2±2.1

12.0±2.0

16.8±2.4

12.8±1.8 18.1±2.7

10.5±2.6

16.6±1.7

13.7±2.3 18.7±2.9

Average

11.0±3.4

17.6±4.0 13.5±5.5 20.0±6.9

ICC

0.53

0.74 0.77 0.70

From these results we can see that participant 7 had

both of their angle measurements rejected (and we

can easily see that the right and left angles are quite

different (16.2 compared to 26.7 and 23.2 compared

to 35.5). This can happen because participant 7 was

the person with the lowest height and weight (1.58

m and 48 kg respectively), and the classification

algorithm can have problems with persons with

anthropometric data far from the average population.

This can also happen when the participant doesn’t

seat symmetrically compared to the air bladders in

posture P1 or they move their pelvis during posture

transitions. There were 2 other measurements that

were rejected (participant 6 – test 1 and participant 3

- test 2), which means that the classification system

can have a small bias to one of the lateral sides. In

the Stable Zone during posture P5/P6, the ANN

classification algorithm does not differentiate

between smaller lateral flexion and larger flexion

(e.g. 20º compared to 35º), as the output of the ANN

is the same for these postures. We implement Fuzzy

Logic to differentiate between these types of

postures and also integrate the adopted time in each

sitting posture.

Table 3: Two-sample KS test at a 1% significance level.

Test 1 checks the null hypothesis that the data from the 1st

angles of P5 and P6 are from the same distribution. Test 2

checks the null hypothesis for the 2nd angles.

Subject Test 1 p-value Test 2 p-value

1 0 1.6786e-02 0 1.6786e-02

2 0 3.0794e-01 0 8.8990e-01

3 0 5.1467e-02 1 2.3766e-04

4 0 5.8861e-02 0 1.3586e-01

5 0 1.6786e-02 0 5.1467e-02

6 1 4.7152e-03 0 3.0794e-01

7 1 8.7713e-07 1 1.0054e-07

8 0 1.3586e-01 0 5.8861e-01

9 0 5.1467e-02 0 5.1467e-02

10 0 9.9832e-01 0 9.9832e-01

11 0 8.8990e-01 0 3.0794e-01

12 0 5.1467e-02 0 1.6786e-02

4 CLASSIFICATION BY FUZZY

LOGIC

To develop the fuzzy logic algorithm, we created

membership functions (shown in figure 5) dependent

on the COP of the backrest and seatpad and the time

spent in each posture. The set of rules are presented

in Table 4. The max time for the time function is

900 seconds (15 minutes), as we will evaluate the

sitting posture every 15 minutes. The interval for

each time membership function is based on ISO

standards for trunk inclination (Standardization,

2000), although those values are for standing

postures instead of sitting postures, so they were

increased accordingly. The maximum values for the

Centre of Pressure were previously mentioned and

are shown in figure 1-C. As an example, if in 15

minutes, the user was found to have been in two

postures P3 and P6 (respectively for 360 and 540

seconds), with an average value for the COPx_seat,

COPy_seat, COPy_back, COPz_back respectively

of [2.38; -1.76; 1.22; -2.17] cm for P3 and [-5.61; -

Fuzzy Logic Toolbox and the Mamdani Centre of

Gravity Defuzzification algorithm (Mamdani E. H.,

1974), we can reach a value of 0.354 and 0.443 (for

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Figure 5: Membership functions of the fuzzy logic algorithm. Dashed lines represent values that are not possible to attain,

but we use a full triangle function to avoid asymmetric distributions. (A), (B), (C), (D) and (E) represent the antecedent

membership functions and (F) represents the consequent membership functions.

Table 4: Set of rules for the Fuzzy Logic Algorithm.

IF (Time = Long) THEN (Posture = Incorrect)

IF (Time = Short) OR (Cxh OR Cxv OR Czv OR Cyh = Center THEN (Posture = Correct)

IF (Cxh = Right flexion) OR (Cyh = Frontal Flexion) THEN (Posture = Incorrect)

IF (Cxv = Right flexion) OR (Czv = Top) THEN (Posture = Incorrect)

IF (Cxh = Left flexion) OR (Cyh = Extension) THEN (Posture = Incorrect)

IF (Cxv = Left flexion) OR (Czv =Bottom) THEN (Posture = Incorrect)

IF (Cxh = Int. Right Flexion) OR (Cyh = Int. Frontal Flexion)) AND (Time = Long) THEN (Posture = Incorrect)

IF ((Cxv = Int. Right Flexion) OR (Czv = Int. Top)) AND (Time = Long) THEN (Posture = Incorrect)

IF ((Cxh = Int. Left Flexion) OR (Cyh = Int. Extension)) AND (Time = Long) THEN (Posture = Incorrect)

IF ((Cxv = Int. Left Flexion) OR (Czv = Int. Bottom)) AND (Time = Long) THEN (Posture = Incorrect)

IF (Cxh = Int. Right Flexion) OR (Cyh = Int. Frontal Flexion)) AND (Time = Accept.) THEN (Posture = Incorrect)

IF ((Cxv = Int. Right Flexion) OR (Czv = Int. Top)) AND (Time = Accept.) THEN (Posture = Accept.)

IF ((Cxh = Int. Left Flexion) OR (Cyh = Int. Extension)) AND (Time = Accept.) THEN (Posture = Accept.)

IF ((Cxv = Int. Left Flexion) OR (Czv = Int. Bottom)) AND (Time = Accept.) THEN (Posture = Accept.)

P3 and P6 respectively). Using these values, we can

now try to implement a fuzzy logic actuator system

to inflate and deflate specific air bladders depending

on the detected postures.

5 CONCLUSIONS AND FUTURE

WORK

In a previous work, a chair prototype was used to

classify 11 standardized seating postures of users,

using 8 air bladders (4 in the chair’s seat and 4 in the

backrest). Here we showed that using industrialized

air bladders, improved the stability of the previously

developed Posture Classification Algorithm, which

was based on Neural Networks integrated with

Decision Trees. One of the identified gaps in that

system was the classification behaviour in

intermediate postures or during posture changing, as

the previous classification was only made in a so

called “stable zone”.

In this paper we studied how the classification

algorithm handled lateral postural changes, and

identified a stability and instability zones. During

lateral flexion, the stability zone was found to be

around an interval of 9º, between 11º and 20º. After

20º, the ANN algorithm is stable in classifying the

lateral flexion postures. To differentiate between

intermediate trunk flexion and extension we devise

an approach based on integrating Fuzzy logic into

the existing Neural Network-based Classification

Algorithm that was capable of classifying 6 standard

sitting positions

For future work we will need to check the

influence of the angle in frontal flexion and during

trunk extension, and lateral flexion during leg

crossing positions. Based on the output of the

SittingPostureDetectionusingFuzzyLogic-DevelopmentofaNeuro-fuzzyAlgorithmtoClassifyPosturalTransitionsin

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Neuro-fuzzy classification algorithm we will devise

a Fuzzy actuation system to create a new Posture

Correction algorithm, since the previous algorithm is

based on simple Boolean logic (Martins et al., 2014).

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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SittingPostureDetectionusingFuzzyLogic-DevelopmentofaNeuro-fuzzyAlgorithmtoClassifyPosturalTransitionsin

aSittingPosture

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