In-chair Movements of Healthy People during Prolonged Sitting

Elisa Marenzi, Gian Mario Bertolotti and Giovanni Danese

Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via Ferrata 3, Pavia, Italy

Keywords: In-chair Movements, Interface Pressure, Centre of Pressure.

Abstract: This paper describes a program designed to detect and give a classification of the in-chair movements done

by healthy people while seated for long periods of time. The purpose of this work is to identify the

frequency, duration and typology of movements performed by subjects that need to remain seated for a

prolonged time. The software finds the time instants of each movement, its duration and whether it is in the

sagittal or the lateral plane; in particular it distinguishes between a left and right movement (in the lateral

plane) and a forward or backward trunk movement. This information can be useful in many different

domains: first of all to monitor the fidgeting phenomenon and consequently the feeling of discomfort in the

office environment; it can be adopted to evaluate the fatigue of car and truck drivers; but the most important

outcome concerns the clinical setting, in which it can be very helpful for the medical staff in determining an

appropriate and personalized rehabilitation strategy for patients with motor limitations in order to prevent

the development of pressure ulcers.

1 INTRODUCTION

Pressure ulcers are defined as areas of localized

necrosis and tissue damage of the skin, that result

from prolonged excessive loading on a support

surface (Mak, Zhang and Tam, 2010). Pressure sores

are frequent in subjects with motor and sensory-

motor limitations; among them, people with spinal

cord injuries, who are thus wheelchair bound. Their

treatment is based on pressure, shear and friction

stress reduction, on attention to bacterial

contamination and on nutritional deficit correction

(Scena and Steindler, 2011). In fact, these

individuals, especially those with limited trunk

stability and limited motor function, sit in a

wheelchair for long, consecutive periods of time,

mostly in one posture. As a consequence, pressure

ulcers can develop because of an unbalanced match

between the external load and the ability of the skin

and subcutaneous tissue to withstand that load

(Reenalda et al., 2009).

Data on prevalence of pressure sores gathered in

America and in Europe help comprehend the

dimension of the phenomenon. Prevalence values in

general are high and have been reported to be 15.5%

in U.S. healthcare facilities, with 28.0% of the

pressure ulcers located at the sacrum and 17.2% at

the buttocks (Vangilder, Macfarlane and Meyer,

2008). In Canada, the total prevalence is estimated

in about 26% (Woodbury and Houghton, 2004),

while prevalence values of 18.1% have been

reported in European standard and academic

hospitals (Vanderwee et al., 2007). In particular, in

The Netherlands and in Germany the prevalence has

been estimated in 11,1% in hospitals and 11,8% in

nursing homes (Tannen et al., 2004).

Current clinical practices for assessing the risk of

pressure ulcers focus in monitoring the intensity and

duration of interface pressure (Sakai et al., 2009),

which is defined as the pressure on the skin

occurring by contact with a surface such as a

mattress or cushion (Braden and Bergstrom, 1987;

Jones, 2005). However, despite the fact that much

research has been performed on the aetiology and

prevention of pressure ulcers, a threshold value for

the development of deep pressure ulcers, in terms of

magnitude or duration, is still lacking (Reenalda et

al., 2009). Moreover, developed pressure time

curves predict that pressure ulcers will develop even

with optimal pressure distribution (Kosiak, 1959;

Reswick and Rogers, 1976).

It is important to note that maintaining a

prolonged seating position is not a natural condition

for human beings, thus the body structure is

seriously affected by the amount of time during

which this position is sustained. However, it is

145

Marenzi E., Bertolotti G. and Danese G..

In-chair Movements of Healthy People during Prolonged Sitting.

DOI: 10.5220/0004723101450152

In Proceedings of the International Conference on Physiological Computing Systems (PhyCS-2014), pages 145-152

ISBN: 978-989-758-006-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

demonstrated that healthy people are able to perform

unconscious mechanisms that preserve the integrity

of tissues (Hermann, 2005; Branton, 1969). The

little postural adjustments done to acquire a new

comfortable posture are identified as “fidgeting”

(Fenety and Walker, 2002; Fenety, Putnan and

Walker, 2000).

From a clinical point of view, the most

significant method for pressure ulcers prevention is

that of training patients to change their posture

frequently (Merbitz et al., 1985); however, there are

not many data on the correct periods for these

manoeuvres, thus useful input can be obtained by

studying the spontaneous kinematic behaviour of

healthy subjects during long periods of sitting

(Scena and Steindler, 2011). In fact, the constant

adjustment of the seated position in general is

realized through numerous movements of reduced

entity on the seat, with the aim of redistributing the

pressure stresses on the seat surface and of searching

comfort. The periodic movements on the support are

denominated in-chair movements (ICM) and they

represent the factor that prevents the development of

decubitus ulcers (Hermann, 2005). Without ICM,

every subject forced to remain in a seated position

for many hours would quickly develop pressure

sores, in particular in the ischial tuberosities (Fenety,

Putnan and Walker, 2000). However, it is not

possible to indicate a specific exposure time interval

that can be stated as the critical time for the

development of such ulcers, because it varies

according to various factors depending on the

subject and on the situation.

For this reason it would be appropriate to execute

a continuous monitoring of the interface pressure

distribution on the seat surface, in order to

immediately identify potential risk situations.

This procedure could be very useful not only in

the clinical environment, but also to evaluate the

fatigue (Nakane, Toyama and Kudo, 2011) and the

discomfort of individuals, in an unobtrusive way,

during office work and during prolonged periods of

car or truck driving (Marenzi, Bertolotti and

Cristiani, 2012).

The aim of this work is to develop a program that

identifies the time instants, duration and type of in-

chair movements executed by a seated subject. The

software has been tested on a group of 10 people to

evaluate the efficacy of the program. After the

introduction to the problem of decubitus ulcers and

their prevention through the evaluation of healthy

sitting behavior, the parameters used for the

movement identification are described, together with

the monitoring device. The third section explains the

detection algorithm, followed by the results obtained

on healthy subjects seated on a foam chair and, at

last, by conclusions and future developments.

2 IN-CHAIR MOVEMENT

DETECTION

The characterization of the behaviour of seated

people concerns the identification of the number,

frequency, duration and type of movements

executed; thus we designed an algorithm that

calculates all these features, given the data regarding

interface pressure distributions recorded during a

prolonged time.

2.1 Centre of Pressure (CoP)

From the analysis of previous studies it can be

observed that the centre of pressure (CoP) is one of

the most used parameters for studying human

posture (Bertolotti et al., 2012) and it is defined as

the point of application of the resultant of the

vertical forces that act upon the support surface

(Hermann, 2005), in this case the seat. It can be

measured in a non-invasive way and there isn’t a

unique method of calculation, because it depends on

the requirements of the specific application (Marenzi

et al., 2012).

In biomechanics, clinical settings, motor and

rehabilitation sciences, the CoP is often used to

study the feet-ground interface of a standing person;

in fact this parameter allows the recording of little

postural sways related to motor control. Other

studies use the CoP to analyse the seated position:

every time a person moves (doing the so-called

ICM) the CoP position changes too.

In this work we decided to use CoP as a

parameter for the monitoring of postural changes

during the seated position, in fact it is demonstrated

that there is a linear relationship between the

localization of CoP and the trunk lateral flexion and

tilt angles (Fenety, Putnan and Walker, 2000). A

change in CoP position can be considered the reflex

of a movement, given the following conditions: the

seat has to support the majority of the body load

(backrest, armrest and ground should have the

minimum contribution); the dynamic components

(such as the accelerations during postural shifts)

should be minimal, in order to consider the seated

posture as a succession of static positions.

When a person moves some body segments, a

pressure redistribution at the seat interface occurs,

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146

for this reason it is possible to register the CoP

movements in a non-invasive way through the use of

sensorized mattresses and this makes this parameter

particularly useful in evaluating the sitting behaviour

of healthy people. The coordinates are calculated as

follows:

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t is the time in which the sample is acquired; n is

the total number of sensors of the mattress; P

(t) is

the pressure on the i

sensor; x

and y

are the

coordinates of the i

sensor

Together with the CoP, the CoP speed, that is the

amount of activity required to maintain stability

(Geurts et al., 1993), is very useful in detecting the

ICM of a seated person; the following equations

show how it is obtained (Bertolotti et al., 2012):

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(5)

2.2 Materials and Methods

The algorithm has been defined using a commercial

sensorised mattress: the purpose of the work is in

fact to develop a solution that is used after the

recording done by the mattress, to evaluate the

seated posture and to give suggestions on the anti-

decubitus device and the repositioning strategy. The

device is the Novel Pliance

Sensor Mat 392 (Novel

Inc.), composed of 256 square capacitive sensors,

distributed on a 16x16 matrix. To obtain the pressure

data useful to develop the algorithm, 10 healthy

subjects sat for one hour on an office chair, with no

armrests and with a 5 cm thickness foam support and

they could not lean on the backrest.

The Pliance Mat records pressure distributions at

a sampling frequency of 50 Hz and produces two

different text files: the first contains the time instant

in which the sample is registered (the first column),

together with the pressure of every sensor (one row

contains a single and complete pressure map); the

second one shows, for every sample, the CoP

coordinates (in centimeters) and the total force

exercised on the mattress. We analysed these files

with Labview and designed a program that is able to

identify the ICM after a test. This program could be

used not only with the Novel Pliance Sensor Mat

but, with minimal adjustments, with every

commercial device that produces ASCII files of the

pressure data. It can be used also with a prototype

device that has been developed in the laboratory in

order to obtain a more robust, but at the same time

less expensive (compared to the other devices on the

market), electronic instrument (Marenzi et al.,

2012).

2.3 Movement Detection Algorithm

The first step in identifying the detection algorithm

is to adjust the ASCII files produced by the Novel

software into a format that is compatible with

Labview. Together with this, it is useful to reduce

the number of samples to analyse, since seated

subjects, as observed in previous works, move at

frequencies approaching 0.5 Hz (Fenety, Putnam

and Walker, 1995). In this way, we created new files

of the interface pressure distributions and of the CoP

coordinates, using a new sampling frequency of 2

Hz, to respect the Shannon Theorem. This procedure

better highlights the pressure variations on the same

sensor and on adjacent capacitors. We also

considered only the CoP coordinates and not the

force column, since the latter is not useful for our

purpose. Once the files are compliant with our

system, we didn’t analyse the first minute of

registration (the first 120 samples) because this is

the period in which a person first seats and reaches

the most comfortable posture.

2.3.1 CoP Speed Calculation and ICM

Identification

After the pre-elaboration previously described, we

calculated the CoP speed, according to equations (3),

(4) and (5), for every sample, in particular the

coordinates are recorded in centimeters using the

lowest point at the right of the matrix as the

reference, as it can be observed in Figure 1. We used

this parameter to set up a threshold that can perform

a first and basic identification of the ICM and we

chose to use 15 cm/s, because we observed that it is

appropriate to discriminate between pressure

changes due to the device reading and effective

movements. Therefore, only if the speed is above

this value, it is considered an ICM and further

processing is done (Figure 2).

In-chairMovementsofHealthyPeopleduringProlongedSitting

147

Figure 1: The image shows a pressure map, recorded with

the Novel Pliance, of one of the subjects used for the tests:

the sensors are numbered from 1 to 256, starting from the

lowest right capacitor, going ahead on the rows. The

column on the right is the pressure legend (only the lower

pressure limit of every range is written, but the matrix can

sense 60 kPa at maximum), while the grey circle

positioned between the tuberosities represents the CoP. In

the image, the ischial tuberosities are clearly visible, with

the highest pressures.

The speed value (when above the threshold) and

the corresponding time are recorded and if two or

more instants are consecutive, they are considered as

part of a single movement and only the instants to

the extremes are stored. From this procedure, we

find all the initial and final instants of the

movements according to the threshold, that form the

rows of a new table. These values however don’t

represent the real time intervals, because the CoP

speed above the threshold represents only a

sufficient condition in detecting the movements. The

later elaborations are necessary in order to define the

true time duration. Every row coincide with one

ICM, thus a portion of the file storing the pressure

maps is extracted, and more specifically, the rows

comprised in the time interval defined by the

instants found, together with the 10 s before the

initial time and the 10 s (20 samples) after the final

value.

From the matrix thus obtained, we have

eliminated three groups of sensors: the ones that are

not loaded or that never change their pressure during

the ICM and the capacitors that show variations of

only ±0,25 kPa (because in this case the difference

in pressure could not be directly correlated with a

movement but it could be due to the instrument). In

the new data set we have found all the indexes of the

Figure 2: This graph, obtained with Labview, shows the

CoP speed during one test (in black), with the threshold

value of 15 cm/s, in red: only the values above the red line

are considered for successive elaborations.

array in which there is a pressure variation and we

have selected three of them: the index that is exactly

at half the time interval, the preceding and the

following ones. This choice is made because at least

one of these values should coincide with an instant

of movement detected by the CoP speed: if this isn’t

the case, that means that the pressure variation is not

related to a movement, thus the corresponding

sensor doesn’t have to be considered. Therefore we

have checked if at least one of these indexes satisfies

the requirement. For every movement in this

category and for every sensor that varies the

pressure in the temporal interval selected, we have

divided the array of the indexes: the first sub-array

contains the first set of indexes, up to the mid-

interval one; the second comprehends all the others.

This is useful to identify the real instants of the

movement: the first element of the second sub-array

is subtracted with the last element of first sub-array

and, if they are not consecutive, it means that the

first index of the second sub-array coincides with the

initial instant of the movement. If this isn’t the case,

this procedure is repeated in decreasing order in the

first sub-array until we found two non-consecutive

indexes; then, the greater of the two represents the

initial instant. An analogous procedure has been

implemented to find the final instant of the ICM but

in this case only the second sub-array is used and we

proceeded from the beginning until we found two

non-consecutive indexes: here, the lesser is chosen

as the final instant. At last, for a single ICM we

obtained two arrays of time indexes: the first

contains the initial value from every sensor

considered and the second stores the final indexes.

To define the real interval, we chose the minimum in

the array of the initial times and the maximum of the

second array. However, since up until now we have

worked only with indexes, we converted the indexes

obtained for all movements in the corresponding

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time values. At the end we found a matrix composed

of two columns: the initial and final time instants of

all ICM. From the observation of these results, it can

be stated that sometimes two or more movements

partially overlap, mainly because the instants

identified with the CoP speed are close to each other

but not consecutive, consequently the system treats

them as separate situations. This however represents

a complex or prolonged movement, therefore in this

case we jointed all these rows to form a single ICM,

with the initial instant coinciding with the initial

value of the first movement and the final instant that

is the final one of the last movement. This procedure

will be completely automated in the next version of

the software.

2.3.2 ICM Classification

With all the ICM identified, we calculated the total

number of movements registered in a test, the

duration of each one of them, the time interval

between successive movements and the typology. A

portion of the file resulting from the elaborations is

shown in Figure 3.

Figure 3: Output file of the movement detection software:

the first two columns represent the initial and final times

of an ICM, the third column shows the type of movement

(as described in detail in Figure 4), the fourth column is

the duration of the movement, while the final column

reports the interval between the current ICM and the

following one.

For this last feature we used again the CoP, in

particular, for every ICM we considered the CoP at

the beginning and we calculated the distance

between this value and all the successive CoP

coordinates during the movement. We selected the

maximum distance and thus the corresponding CoP

coordinates, in order to calculate the tangent of the

angle.

According to the value obtained, we considered

four different movements (each of them indicated

with a code number), two in the lateral and two in

the longitudinal plane (Figure 4): a movement on the

right side of the body; a movement on the left side; a

movement forward; a movement backward.

Figure 4: The image shows how the movements are

classified according to the tangent value: if the angle is

between ±45° the ICM is forward (on the longitudinal

plane, with code number of “2”); if the angle is between

45° and 135° the movement is lateral on the right side

(code “1”); if the angle is between 135° and -135° the

ICM is backward (“-2”) and if the angle is between -135°

and -45°, the movement is lateral on the left (code “-1”).

3 EXPERIMENTAL TESTS

3.1 Evaluation Tests

After completing the program, it was necessary to

test its correct functioning. For this reason, we

developed a protocol for a simple but effective

evaluation: two of the 10 healthy subjects engaged

for the successive tests (a man and a woman) sat on

the same foam chair and were asked to perform the

four ICM described at the end of chapter 2.3 in the

order in which they are written, and at prefixed time

instants (with a time interval of at least 10 s between

consecutive movements). An operator observed the

execution of the test while indicating to the subject

the instant in which to perform the repositioning and

the type of movement. Before the execution of the

first movement, after performing the last one and

between two consecutive displacements the person

remained still for 10 s or more. This sequence of

movements was repeated 10 times in each test,

resulting in 40 movements per person. The aim of

these experiments was to compare the results of the

software with the experimental conditions of the

protocol and then to determine the efficacy of the

software. The results are shown in Table 1: in one of

the subject all the movements are detected and

classified correctly, while in the other experiment

the first movement is seen as two separated ICM,

thus 41 displacements are recorded. In the second

subject the detection of lateral ICM, and in particular

between right and left displacements, is good with

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149

only one ICM (the first one is not a real ICM) on the

right side is considered as a left side displacement,

compared with the identification of forward versus

backward movements.

Table 1: Number, time duration, interval between

consecutive movements and typology for both subjects

involved in the tests.

ICM Subject 1 Subject 2

Total number 40 41

Lateral, right side 10 9

Lateral, left side 10 12

Forward 10 18

Backward 10 2

Time duration 1.675 3.85

Time interval 13.55 11.67

Probably subject 2 performed the longitudinal

movements with slight oscillations of the trunk that

resulted in an incorrect classification of the ICM.

However, in general, the program can detect all the

ICM performed and it can discriminate between

movements in the lateral plane from those in the

sagittal plane. Lastly, regarding the intervals

between movements, the seconds subject shows a

mean value that is closer to the 10 s indicated, but in

both cases, the values are near the reference. One

ulterior important comment is that the 10 s time

interval is not reported by a watch or a similar

instrument as the fixed amount of time that has to

elapse between two consecutive movements, but

represents only the minimum time that has to pass

before executing the new ICM; thus values greater

than 10 s are correct.

3.2 Tests on Healthy Subjects

The program has been used on a group of 10 healthy

subjects that were asked to seat on a foam chair for 1

hour while performing some office task at the PC

(e.g. reading a document, browsing the Internet).

The group is composed of 5 men and 5 women, with

age of 37 ± 12 years. We recorded height, weight

and BMI (Body Mass Index) for everyone, as shown

in Table 2, while the total number of ICM done by

the subjects during the tests and the statistical values

are shown in Tables 3 and 4.

In all cases the standard deviations are high, even

when the two subjects with highest number of ICM

are eliminated from the calculus. This indicated that

the number of movements (and thus the feeling of

discomfort) is highly subjective, thus it is not

sufficient to evaluate the behaviour of seated people.

Maybe the number of ICM performed is correlated

with some anatomical characteristics of the subjects:

Table 2: Physical characteristics of the subjects tested; the

first column indicates the gender.

SUBJECT WEIGHT (kg) HEIGHT (m) BMI

M 1 65 1.74 21.47

F 1 59 1.68 20.9

M 2 65 1.77 20.75

M 3 90 1.85 26.3

M 4 70 1.85 20.45

M 5 85 1.77 27.13

F 2 55 1.70 19.03

F 3 53 1.65 19.47

F 4 55 1.68 19.49

F 5 65 1.63 24.46

Table 3: Total number of ICM performed by the test

people during the 1hour recordings.

SUBJECT NUMBER OF ICM

M 1

F 1

M 2

167

M 3

M 4

M 5

F 2

F 3

F 4

F 5 102

Table 4: Mean and standard deviation for all the subjects

and for the two genders, also without one subject in both

genders that is considered an outlier because the number

of ICM is significantly higher compared to the other

people.

SUBJECTS MEAN STANDARD

DEVIATION

all

62.4 47.04

males

63.4 46.11

females

58 31.55

all with outliers

53.5 30.59

males no outliers

55.1 30.07

females no outliers

55.6 28.61

to verify this hypothesis, a new set of experiments

are planned and related results will be presented in a

further publications. The duration of the ICM also

has been evaluated (Table 5): depending on the

person, the interval of movement can be

homogeneous or quite different, but in this case

there is no such difference among the subjects, like

in the case of the number of ICM. In particular, it

can be observed that the mean duration varies

between 2.32 s and 6.74 s, but the majority of the

people performs movements that last about 4-5 s in

general. Regarding the time interval between

consecutive movements (Table 6), it can be noted

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150

that the lower the number of ICM done, the higher

the time intervals are, and vice versa. This leads to

conclusions analogous to the case of the total

number of movements: in fact, among the people

involved in these tests there is high variability. At

last, we considered the typology of ICM executed by

every subject (Table 7): in general, the most

common ICM are the lateral displacements (372 out

of 618, more than half the total movements), with

prevalence on the right side. This is a symptom that

the ischial tuberosities, being the areas with the

highest pressures, in a prolonged seated position,

need to be unloaded and the lateral ICM seems to be

the better solution.

Table 5: Mean time duration and statistical parameters of

all the ICM, for every subject.

SUBJECT MEAN

(s)

STANDARD

DEVIATION

M 1 6.74 5.54

F 1 2.32 2.45

M 2 4.24 5

M 3 5.69 2.76

M 4 4.11 2.8

M 5 3.5 2.83

F 2 4.42 3.2

F 3 3.33 1.9

F 4 3.22 3.77

F 5 2.71 2.07

Table 6: Mean time intervals between consecutive

movements for every subject.

SUBJECT MEAN

(s)

STANDARD

DEVIATION

M 1 6 5.87

F 1 182.44 397.57

M 2 17.19 19.87

M 3 102.77 91.9

M 4 39.01 39.49

M 5 123.75 139.41

F 2 57.23 68.57

F 3 130.08 181.3

F 4 43.32 44.55

F 5 31.9 42.65

However, also the discomfort plays a role when a

person seats for long periods of time, therefore even

movements in the sagittal plane are necessary. The

fact that the forward displacements are fewer than

those backward, can be explained by remembering

that the subjects could not lean on the backrest, thus

they were induced in a curved forward position;

therefore, the better way to relieve the spinal column

was to move backward.

Table 7: Total number of movements for every typology,

for every subject and in all the tests.

Subject Right

lateral

Left

lateral

Forward Backward

M 1 13 6 6 10

F 1 6 7 1 5

M 2 76 52 5 34

M 3 5 9 2 2

M 4 32 19 12 21

M 5 12 7 3 7

F 2 11 18 8 22

F 3 2 2 5 18

F 4 23 19 21 28

F 5 34 19 21 28

Total 214 158 74 172

4 CONCLUSIONS AND FUTURE

DEVELOPMENTS

This paper describes a program that identifies the in-

chair movements, that can be used in various

domains: from the clinical environment to prevent

pressure sores, to the automotive field to evaluate

fatigue and discomfort, and in ergonomics for the

design of office chairs and armchairs. In particular,

regarding the clinical setting, it is believed that

studying the behaviour of healthy seated people

could be useful and important in evaluating effective

strategies for the prevention of pressure ulcers

(mainly in the decubitus area). The software is able

to detect the ICM and records the time extremes of

each movement, its duration, the typology and the

interval between consecutive movements. It has

been tested on two subjects to evaluate the

effectiveness of the algorithm and after that, 10

healthy people were enrolled in a prolonged test.

The results show that the system can correctly detect

the movements and also differentiate between

different displacements, thus it can be implemented

with bigger sets of pressure data. Future

developments of the program can involve a better

detection of the appropriate movement typology,

with the aim of finding a more precise way to define

it. The software will be also applied for further

experimental tests with healthy people, in order to

better characterize their behaviour in many more

conditions. Regarding medical applications, the

program will be implemented in clinical experiments

involving patients hospitalized in rehabilitation

structures, in order to evaluate and compare the

behaviour of people with motor limitations and

disabilities. The aim is twofold: on one hand it is

very important to prevent the development of

In-chairMovementsofHealthyPeopleduringProlongedSitting

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decubitus ulcers, thus it is necessary to find the

appropriate and personalized prevention strategy,

that can involve self-repositioning and the use of

new devices for the redistribution of the body load

on the seat support. On the other hand, this program

can be useful for the medical personnel to monitor

the various phases of the rehabilitation and to help in

evaluating the progresses made.

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