Analysis of Postural Variability of Ofﬁce Workers Using Inertial Sensors

abio Mendes

, Phillip Probst

1 a

, Eduarda Oliosi

1,2 b

, Lu

ıs Silva

1 c

, C

atia Cepeda

1 d

and Hugo Gamboa

1 e

LIBPhys (Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics),

NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal

Faculty of Sports of the University of Porto, Porto, Portugal

Keywords:

Postural Variability, Posture, Musculoskeletal Disorders, Inertial Sensors, Ergonomics, Ofﬁce Work.

Abstract:

Musculoskeletal disorders signiﬁcantly impact workers in terms of quality of life, result in low organisational

productivity, and high insurance costs in society. Postural changes have been suggested as a prerequisite to

prevent musculoskeletal disorders. This paper examines the differences in postural changes of forty ofﬁce

workers in a real working environment using a smartphone’s inertial sensors. Through these data, several

variables considered to characterise postural changes while sitting were extracted. Features based on the

number of changes and different postures, time spent and distance covered within a posture showed signiﬁcant

differences in both time of the day (morning and afternoon) and day of the week (start and end of the week).

These results conﬁrm that accumulated working time inﬂuences a person’s postural changes and could have a

potential use for worker’s ergonomic occupational risk evaluation.

1 INTRODUCTION

People that are part of the working population spend

a signiﬁcant time of their daily lives at work. Good

conditions must be ensured to provide workers a safe

environment in which they feel conﬁdent and where

occupational risks and the onset of work-related dis-

orders (WRDs) are kept at a minimum, contribut-

ing to productivity and economic development (World

Health Organization, 2017). Exposure to risk fac-

tors such as heat, noise, and posture issues can di-

rectly contribute to causing diseases or aggravate

some health conditions. Additionally, stress and some

determinants at social relations at work also nega-

tively affect workers’ health (World Health Organi-

zation, 2017; Hulshof et al., 2021). Therefore, it is

essential to monitor and prevent WRDs to create a

safe environment and promote health at work. Strate-

gies such as the improvement of the conception of the

workplace and the education on keeping a proper pos-

ture can be very effective to reduce the risk of one

of these disorders (World Health Organization, 2017;

https://orcid.org/0000-0003-3239-9813

https://orcid.org/0000-0003-1002-4295

https://orcid.org/0000-0001-9811-0571

https://orcid.org/0000-0002-2998-976X

https://orcid.org/0000-0002-4022-7424

Perista et al., 2016).

Especially, occupations such as computerised of-

ﬁce work are characterised by long-lasting low in-

tensities, static postures, and repetitive actions (Srini-

vasan and Mathiassen, 2012). Consequently, differ-

ent metrics and tools for measuring workers’ move-

ment have been assessed at various levels, includ-

ing kinematic components and neuromuscular pat-

terns. In healthy people, functional tasks are natu-

rally performed with variable motor patterns, illustrat-

ing an inherited normal variation in space and time to

preserve or achieve functional skills (Srinivasan and

Mathiassen, 2012). Nevertheless, in the presence of

musculoskeletal disorders (MSDs), people often show

different motor control strategies, and changes in mo-

tor variability are often reported in kinematic param-

eters (e.g., reduced degrees of freedom during walk-

ing or other activities of daily living) and neuromus-

cular variables (e.g., reduced variability of muscle

activity during repetitive lifting or other tasks) (Al-

subaie et al., 2021). Thus, variation in movements,

posture or muscle activity has been a prerequisite to

prevent musculoskeletal complaints during functional

tasks (Mingels et al., 2021).

The primary purpose of this research study is to

analyse the postural changes of public administration

workers in their natural work environment. The study

Mendes, F., Probst, P., Oliosi, E., Silva, L., Cepeda, C. and Gamboa, H.

Analysis of Postural Variability of Ofﬁce Workers Using Inertial Sensors.

DOI: 10.5220/0011688500003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS, pages 273-280

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

273

focuses on sedentary work. Data were collected us-

ing the inertial sensors of a smartphone placed on the

subjects’ chest. This work is part of the PrevOccupAI

project (Biosignals LIBPhys-UNL, 2020), which has

the support of the Portuguese Autoridade Tribut

aria

e Aduaneira (AT) and Direc¸

ao-Geral da Sa

ude. The

main goal of this project is to promote occupational

health and prevent WRDs, through the identiﬁcation

of risk factors in the ofﬁce context.

2 RELATED WORK

Previous research uses several observational and data-

mining techniques for occupational risk assessments.

For example, Carnide et al. evaluated possible causes

for MSDs through questionnaires and clinical exams,

including electromyography (EMG) (Carnide et al.,

2006). EMG sensors were used to compare mus-

cle activation during computer tasks in those with

and without pain in computer workers (Kelson et al.,

2019) and also to quantify the spatio-temporal ef-

fects of biofeedback by inducing active and passive

pauses on the trapezius activity patterns using high-

density sEMG sensors in computer work (Samani

et al., 2010).

Ryan and colleagues objectively investigated

workplace sedentary behaviour and adherence to cur-

rent recommendations via accelerometer in a popu-

lation of ofﬁce workers (Ryan et al., 2011). Lenzi et

al. developed a toolbox to support expert video analy-

sis of manual handling of low loads at high frequency

through the use of inertial sensors (Lenzi et al., 2018).

Prior studies have used different technologies to

characterise posture changes in sitting posture, such

as: video analysis during laptop tasks (Mingels et al.,

2021); instrumented ofﬁce-chairs to explore the re-

lation to the development of perceived discomfort

(Søndergaard et al., 2010) and detect the difference

between ages (Madeleine et al., 2021); textile pres-

sure mat to observe the characteristics of movement

patterns during a prolonged sitting bout and to de-

termine their association with musculoskeletal pain

(Arippa et al., 2022) and other concerning problems

such as back pain in ofﬁce workers (Bontrup et al.,

2019); motion analysis system to quantify the self-

reported discomfort in a stool, a computer chair, and

a gaming chair (Chen et al., 2021) during sustained

ofﬁce work via wireless inertial motion sensors (Jun

et al., 2019).

The mentioned studies all focus on using highly

specialised equipment that are often expensive. In this

paper we aim at utilising equipment that is available

to all workers without extra costs by using a smart-

phone placed on the subjects’ chest as a data acqui-

sition tool. Furthermore, to the best of our knowl-

edge, there are no studies that explore the evolution of

postural changes in ofﬁce workers through the smart-

phone’s inertial sensors.

3 DATASET

3.1 Participants

The acquisition sessions were performed with ofﬁce

workers from AT, working in a real-world scenario

at their own workplace. The participants performed

their regular ofﬁce work, and each participant was

monitored while working for ﬁve consecutive days,

sitting at their desk.

These sessions took place at four different AT di-

visions and in four different weeks. There were a total

of 40 participants, 10 for each AT division. The par-

ticipants’ age was 51 ± 5 years and the overall body

mass index was 25.34 ± 4.40 kg/m

, 24.78 ± 4.79

kg/m

for females (n = 27) and 26.71 ± 3.03 kg/m

for males (n = 11).

3.2 Experimental Setup

The study was conducted in order to collect inertial

data from people working at their desks. A smart-

phone was placed on the chest of each participant,

using a special strap around the neck and torso, ac-

cording to Figure 1. This conﬁguration ensured that

the smartphone’s y-axis was pointing up.

Figure 1: Smartphone placement for data acquisition.

The smartphones used are Xiaomi Redmi Note 9

models (Xiaomi Inc., www.mi.com), which include a

variety of sensors, such as accelerometer, gyroscope,

magnetometer, and rotation vector.

Using the PrevOccupAI mobile application (Silva

et al., 2022), a total of four acquisitions were sched-

uled for each of the ﬁve days. These included two

acquisitions in the morning and two in the afternoon,

to allow an analysis of the acquired signals through-

out the day and the week. The sampling rate was

set to 100 Hz and the acquisition time to 20 minutes.

BIOSIGNALS 2023 - 16th International Conference on Bio-inspired Systems and Signal Processing

274

Thus, while participants were working, the acquisi-

tions started and ended automatically.

4 METHODS

4.1 Data Pre-Processing

The smartphone runs the Android operating system,

which is designed to prioritise battery saving. That

can lead to the sensors starting at different times, sam-

pling asynchronously, and using a non-equidistant

sampling procedure. Hence, the acquired sensors of a

device become misaligned in time and therefore it is

necessary to resample all signals to the same equidis-

tant sampling rate and crop them to the same size. As

the acquisitions included accelerometer, gyroscope,

magnetometer, and rotation vector, the procedure was

done for all of them so that their signals could be anal-

ysed simultaneously.

The ﬁrst step is to deﬁne the starting and stopping

points, and crop or pad the signals according to that.

For this work, we chose the starting point as the ini-

tial timestamp of the last sensor that started acquiring,

and the stopping point as the ﬁnal timestamp of the

ﬁrst sensor that stopped acquiring. The differences

between the starting and stopping times of the sensors

usually do not exceed a couple of seconds.

After cropping, each signal has to be individu-

ally resampled, to assure that the sampling frequency

is constant and the same for all sensors. Therefore,

a new time axis with constant intervals was gener-

ated, beginning and ending at the deﬁned starting and

stopping points, respectively. Then, each signal was

individually interpolated, using the new time axis.

This way, the smartphone sensors data were ﬁnally

aligned. The new sampling rate was set to 100 Hz

and a linear interpolation was performed.

4.2 Removal of Non-Sitting Periods

As acquisitions were performed in a real-world sce-

nario, it is possible that the participants did not remain

seated for the entire acquisition period.

To ensure the validity of the analysis to be per-

formed, it was important to develop an algorithm to

detect the periods when a participant was not seated

and remove these from the data. For this purpose,

we performed some additional acquisitions of a per-

son sitting and walking (using the same setting) and

trained a machine learning model which detects when

a participant is not seated. This model is based on the

random forest algorithm and reached an accuracy of

100% using a 70/30% split with ﬁve different seeds.

To apply this machine learning model, the smart-

phone’s accelerometer signals were ﬁrst ﬁltered using

a smoothing ﬁlter with a Hanning window of 30 sam-

ples. Then, the signals were divided into windows of

5 seconds and both statistical and temporal features

were extracted from each signal window. Using these

features, each window was classiﬁed by the model as

sitting or walking. Finally, the windows classiﬁed as

walking were removed from the signals to analyse.

This way, the variables related to postural variability

while sitting could be extracted.

4.3 Extraction of Postural Variables

After pre-processing, we deﬁned a set of variables

that we considered representative of postural variabil-

ity. This postural variability refers to the adjustments

each person makes to their sitting posture. Posture is

deﬁned as the position where we keep our body when

we are seated. The considered variables include:

• Number of changes in posture;

• Number of different postures;

• Mean time of transition between postures;

• Time spent in each of the subject’s three most

common postures;

• Time spent in the remaining postures;

• Total distance covered;

• Distance covered in each of the subject’s three

most common postures;

• Distance covered in the remaining postures;

• Variance in each of the subject’s three most com-

mon postures;

• Mean variance in the remaining postures;

• Mean velocity;

• Mean velocity in each of the subject’s three most

common postures;

• Mean velocity in the remaining postures.

To extract these variables from the available data,

we used the smartphone’s rotation vector, which al-

lows the calculation of the subject’s trunk position at

each moment. The rotation vector sensor merges ac-

celerometer, gyroscope, and magnetometer data, and

is based on the mathematical concept of quaternions,

which is the description of 3D orientation using a

4D complex number system (Goldman, 2011). Thus,

the smartphone’s rotation vector returns four values

that describe the phone’s orientation relative to the

phone’s base coordinate system, which is illustrated

in Figure 2.

Analysis of Postural Variability of Ofﬁce Workers Using Inertial Sensors

275

Figure 2: Coordinate system of the smartphone.

These quaternions were ﬁrst converted to Euler

angles, and the median was subtracted and considered

as the reference point. The Euler angles were then

transformed into positions in the xz-plane (accord-

ing to Figure 2), which corresponds to the horizon-

tal plane when the subjects have the phone placed on

their chest. The x and z coordinates allow the determi-

nation of the inclination of the trunk in that plane, at

a given moment, which deﬁnes the different postures

of each person. To allow the comparison of postures

between subjects, these coordinates were normalised

by the height of each individual.

Furthermore, it was also necessary to categorise

the different possible postures into ﬁnite ranges. Tak-

ing into account that the obtained x and z coordinates

ranged from -1 to 1, the xz-plane was divided into a

grid of equal squares (7x7), whose dimensions were

manually chosen to optimise the number of different

postures, as represented in Figure 3. The grid was

equally distributed in both directions, consisting of 49

different possible postures. However, some of those

49 intervals are humanly impossible to reach.

Figure 3: Division of the xz-plane to deﬁne the possible

postures.

Moreover, some of the extracted variables

required the implementation of additional pre-

processing tools. To extract the variables not in-

volving variability within the same posture (number

of changes in posture, number of different postures,

mean time of transition between postures, time spent

in each of the subject’s three most common postures,

and time spent in the remaining postures), the pos-

tural sway behaviour was removed. This behaviour

corresponds to the small and unconscious movements

around the body’s center of mass needed to main-

tain balance while standing or sitting (Paterno et al.,

2013). This postural sway, if not removed, can cause

oscillations between the limits of two of the deﬁned

postures, affecting the extracted variables. These

small oscillations are unconscious and do not involve

changes in posture, as they are only adjustments that

each individual makes to their posture. For this rea-

son, to extract some of the variables, the higher fre-

quencies were removed from the signals by applying

a low-pass ﬁlter with a cut-off frequency of 0.3 Hz

(Soames and Atha, 1982) to the Euler angles.

To allow comparison between subjects, some of

the 21 variables had to be normalised. The number of

changes in posture was normalised by an hour, while

some of the variables involving time (time spent in

the most common, second most common, third most

common, and remaining postures) and the variables

involving distance (total distance and distance cov-

ered in the most common, second most common, third

most common, and remaining postures) were nor-

malised by the time of acquisition (approximately 20

minutes).

4.4 Analysis of Postural Variables

From the four acquisitions per day, for the purpose of

this study, we extracted the ﬁrst in the morning of the

ﬁrst day, the last in the afternoon of the ﬁrst day, the

ﬁrst in the morning of the ﬁfth day, and the last in the

afternoon of the ﬁfth day (4 out of the 20 acquisitions

of each subject). Thus, the statistical analysis allows

to evidence the evolution of each variable throughout

the day and throughout the week.

A two-way repeated measures analysis of vari-

ance (ANOVA) test was applied to each dependent

variable considering as levels the time points of the

day (morning and afternoon) and days of the week

(ﬁrst and ﬁfth). The p-values were corrected by the

Greenhouse-Geisser method. Normality assumption

was considered under the Central Limit Theorem and

the level of signiﬁcance was set to 5%.

5 RESULTS

Table 1 presents the results of the two-way repeated

measures ANOVA for time of the day (Time) and day

of the week (Day), and their respective interaction.

Figure 4 displays the evolution of the mean values

of the variables that showed statistically signiﬁcant

differences and/or signiﬁcant interaction according to

Table 1.

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276

Table 1: Statistical analysis of the extracted postural variables by two-way repeated measures ANOVA.

Variable

Comparison Comparison Interaction

(Time) (Day) (Time × Day)

Number of changes in posture 0.014* 0.004* 0.274

Number of different postures 0.010* 0.011* 0.468

Mean time of transition between postures 0.761 0.066 0.396

Time spent in the most common posture 0.002* 0.154 0.423

Time spent in the second most common posture 0.351 0.927 0.152

Time spent in the third most common posture 0.067 0.776 0.595

Time spent in the remaining postures 0.001* 0.008* 0.631

Total distance covered 0.330 0.000* 0.799

Distance covered in the most common posture 0.074 0.017* 0.152

Distance covered in the second most common 0.936 0.935 0.386

posture

Distance covered in the third most common 0.861 0.793 0.181

posture

Distance covered in the remaining postures 0.001* 0.002* 0.089

Variance in the most common posture 0.022* 0.846 0.950

Variance in the second most common posture 0.752 0.017* 0.305

Variance in the third most common posture 0.088 0.085 0.080

Mean variance in the remaining postures 0.056 0.118 0.655

Mean velocity 0.657 0.003* 0.104

Mean velocity in the most common posture 0.171 0.177 0.369

Mean velocity in the second most common 0.895 0.029* 0.805

posture

Mean velocity in the third most common posture 0.074 0.425 0.433

Mean velocity in the remaining postures 0.069 0.029* 0.027*

*p-value signiﬁcant at α = 0.05 level.

Figure 4: Evolution of the variables’ mean values throughout the day and throughout the week.

Analysis of Postural Variability of Ofﬁce Workers Using Inertial Sensors

277

Figure 4: Evolution of the variables’ mean values throughout the day and throughout the week (cont.).

BIOSIGNALS 2023 - 16th International Conference on Bio-inspired Systems and Signal Processing

278

6 DISCUSSION

The current investigation evaluated the postural vari-

ability of public administration workers in a seated

posture, through variables extracted from the smart-

phone’s rotation vector sensor, which was placed on

the subjects’ chest. As the acquisitions were carried

out during a working week for each participant, it was

possible to study the evolution of postural changes

throughout the day and week.

Regarding the different periods of the day (morn-

ing and afternoon), shown in Table 1, 6 out of the

21 variables present signiﬁcant differences between

the two time periods. These include the number of

changes in posture, number of different postures, and

time spent in the most common posture. Taking into

account Figure 4, it can be seen that the number of

changes in posture and number of different postures

increase from the morning to the afternoon, while

the time spent in the most common posture decreases

from the morning to the afternoon. These results are

in accordance with the literature, which demonstrates

a need to change posture throughout the working day

(Søndergaard et al., 2010; Jorgensen et al., 2012;

Son, 2017; Forsman et al., 2007). This is due to the

fact that long periods of sitting lead to increased dis-

comfort, which makes people move more and change

their posture (Søndergaard et al., 2010). This means

that postural variability (changes) tends to increase

throughout the day, as a strategy to resist accumulated

tiredness and discomfort.

Concerning different days of the week (Monday

and Friday), Table 1 shows that 10 out of the 21 vari-

ables present signiﬁcant differences between the ﬁrst

and ﬁfth days. These, in addition to including the

number of changes in posture and number of differ-

ent postures, also include some variables related to

distance (total distance covered, distance covered in

the most common posture, and distance covered in the

remaining postures). Figure 4 shows that all these 5

mentioned variables present an increase from Mon-

day to Friday. This is because the subjects also accu-

mulate fatigue throughout the week, increasing their

general movement and postural variability. This ac-

cumulation of tiredness is more noticeable throughout

the week than throughout the day, as evidenced by the

number of variables that show signiﬁcant differences.

Finally, Table 1 also shows that interaction be-

tween the independent variables Time and Day was

not statistically signiﬁcant for 20 out of 21 postural

variables. These results demonstrate that the relation-

ship between time of the day and each of the postural

variables is not inﬂuenced by the day of the week, and

also that the relationship between day of the week and

each of the postural variables is not inﬂuenced by the

time of the day. This reinforces the validity of the

measurements performed.

7 CONCLUSION

In this work, we analysed the postural variability of

ofﬁce workers through inertial smartphone data col-

lected in real context. For this purpose, we performed

a 40-subject study with public administration workers

performing ofﬁce work. From the collected data, 21

variables characterising postural changes while sitting

were extracted.

The postural variables were statistically analysed

to understand their evolution during a work day and

a week. Some of the variables presented statisti-

cally signiﬁcant differences between the morning and

the afternoon, but the ﬁrst and ﬁfth days presented

more variables with signiﬁcant differences. These

results evidence the accumulated fatigue throughout

the day and the week. Regarding interaction between

the independent variables, only one postural variable

presented interaction between the variables Time and

Day.

The results obtained can be used as a means to

help assign a degree of ergonomic occupational risk

to each subject, which can be employed to build a tool

that automatically assesses the occupational risk of a

worker. This risk may then be used to make recom-

mendations to ofﬁce workers, such as short standing

breaks or changes in posture.

In the future, to ensure the validity of the analy-

sis performed, it is important to extend the study to

more subjects, and to collect data over more working

days. Additionally, the set of participants should in-

clude more diverse age groups, and potentially differ-

ent working populations, including workers who do

not work in public administration. Furthermore, pos-

tural variables that encompass time series variability

in postural sway using nonlinear analysis should be

considered.

ACKNOWLEDGEMENTS

This work was partly supported by Science and Tech-

nology Foundation (FCT), under the project PRE-

VOCCUPAI (DSAIPA/AI/0105/2019). The authors

declare that there are no conﬂicts of interest.

Analysis of Postural Variability of Ofﬁce Workers Using Inertial Sensors

279

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