DEGREE PROJECT IN MEDICAL ENGINEERING,SECOND CYCLE, 30 CREDITSSTOCKHOLM, SWEDEN 2019

Evaluation of a Sensor-Based
System for Ergonomic Risk
Assessment among Hairdressing
Students
Utvärdering av ett sensorbaserat system för
ergonomisk riskbedömning i frisörarbete
SHIVA MOKHBERIKTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ENGINEERING SCIENCES IN CHEMISTRY,
BIOTECHNOLOGY AND HEALTH

Evaluation of a Sensor-Based System for Ergonomic
Risk Assessment among Hairdressing Students

Utvärdering av ett sensorbaserat system för
ergonomisk riskbedömning i frisörarbete

Abstract
Occupational upper extremity disorders have become a major issue in modern society. Poorly
designed workplaces, high job demands, and incorrect work-habits can lead to the developmentof upper extremity disorders (UEDs) in the workplace. This issue not only causes health-related
problems for the individual but also forms a significant economic burden on society due to sickleaves, healthcare and untimely exit of affected individuals from the workforce.
The risk of developing occupational UEDs varies with different professions. The European
Agency for Safety and Health at Work (EU-OSHA) has recognized significant occupational
health risks associated with the hairdressing profession. It has been estimated that UEDs are
five times more prevalent amongst hairdressers than other professions.
Qualitative risk assessment tools based on self-reports and observation have been used to
identify the risks of developing UEDs with hairdressing profession before. However, aquantitative risk assessment tool that provides objective data on work posture is more precise
and objective than self-report and observation. This data can help to identify the risks ofdeveloping UEDs associated with each hairdressing task. Furthermore, it can enable selfassessment of workload and posture awareness by providing feedback to the user.
Inertial Measurement Units (IMU) as part of a wearable system developed at KTH were usedin this study to investigate the risks of developing UEDs for hairdressing students. The
feasibility of using a feedback function for providing posture awareness was also evaluated bycomparing the measurements obtained with and without using the feedback function. Twelve
hairdresser students were enrolled in the study.
The percentage of time for elevated angles above 30°, 60° and 90° for arms, and above 45° orless than 0° for the trunk flexion is presented. In addition, 10th and 90th percentiles (°) of arms
and trunk angular distribution is presented. The result of a statistical analysis performed on datawith and without feedback was used to evaluate the effectivity of using the feedback functionin preventing the development of occupational UEDs. A System Usability Scale (SUS)
questionnaire was used to evaluate the overall usability of the system.
The result of this study confirms that the hairdressing profession falls in the high-risk category
for developing UEDs. The use of this technical system has enabled a precise risk assessment
evaluation of each hairdressing task. Such data can be used as a foundation for improving theergonomic design of the workplace. The feasibility of using the feedback function as aprevention tool on the individual level is highly dependent on the individuals’ motivation andtheir attitude towards changing their work habits. However, the results in general, indicate adecrease in the abduction angle (°) for both left and right arm when the feedback function isused. For example, the 90th percentile abduction angles (°) for left arm (all 12 subjects) during
the drying part of one fundamental work-cycle decreased from a value of 60.4° to 58.2° when
the feedback function was used. The 90th percentile abduction angles (°) for the right arm during
the same part of the fundamental work-cycle decreased from an angle of 53.1° to 51.4°.
The SUS score of 75.6 indicates good overall usability for the system.

2

Sammanfattning
Besvär i det muskuloskeletala systemet i överkroppen som uppkommer på grund av påfrestande
arbetsställningar och icke-optimala arbetsvanor blir allt vanligare i det moderna samhället.
Besvären orsakar inte bara hälsorelaterade problem för individen utan även en avsevärd
ekonomisk börda för samhället.
Risken för att utveckla skador i överkroppen varierar med olika yrken. Europeiska
arbetsmiljöbyrån (EU-OSHA) har identifierat betydande hälsorisker i samband med
frisörarbete. Det har uppskattats att besvär i överkroppen är 5 gånger mer förekommande hosfrisörer jämfört med andra yrken.
Det finns många forskningsprojekt som har använt kvalitativa riskbedömningsverktyg,
baserade på självrapportering och observation, som identifierar riskerna med att utveckla skadori överkroppen bland frisörer. Ett kvantitativt riskbedömningsverktyg som ger objektiva data om
arbetsställning är dock mer exakt än självrapportering och observation. Ett sådant verktyg kan
hjälpa till att identifiera risken för skadeutveckling i överkroppen.
Inertial Measurement Units (IMUs) är en del av ett bärbart mätsystem som har utvecklats på
KTH. Systemet användes i denna studie för att identifiera risken för skadeutveckling iöverkroppen bland frisörer. Riskidentifieringen gjordes genom att mäta vinkel på armar och
rygg. Mätsystemet har även en inbyggd återkopplingsfunktion som uppmärksammar
användaren om deras kroppsställning. Effektiviteten av att använda återkopplingsfunktionen
för att förebygga jobbrelaterade skador utvärderades genom jämförelse av mätningar somerhållits med och utan återkopplingsfunktion. Tolv frisörstudenter deltog i studien.
Överkroppspositionen definierades av vinklar över 45° eller mindre än 0° från en position där
ryggen är rak. Abduktionsvinklar över 30°, 60° och 90° mättes för armar. Tidsperioden för
dessa vinklar d.v.s. hur lång tid överkroppen hölls i dessa vinklar räknades. Armar ochöverkroppvinklar för 10:e och 90:e percentilen (°) samt resultat av en statistisk analys somutfördes på data samlade med och utan återkopplingsfunktionen presenterades. Analysen
utfördes för att utvärdera hur effektiv återkopplingsfunktionen är för att förhindra
arbetsskadeutveckling. En System Usability Scale (SUS) frågeformulär användes för att
utvärdera systemets övergripande användbarhet.
Resultatet av denna studie bekräftar att frisöryrket faller i högriskkategorin för
arbetsskadeutveckling. Användningen av detta bärbara mätsystem har möjliggjort en exakt
riskbedömning för olika arbetsuppgifter. En sådan information kan användas som grund för att
förbättra ergonomiska förhållanden på arbetsplatser. Effektiviteten av återkopplingsfunktionen
som ett förebyggande verktyg på individnivå är starkt beroende av individernas motivation ochderas inställning till att ändra sina arbetsvanor. Emellertid anger resultaten en generell
minskning av armvinklar (°) för både vänster och höger arm när återkopplingsfunktionen
används. Till exempel har den 90:e percentil vinklarna (°) för vänsterarm (alla 12 personer)
under hög belastning minskat från ett värde av 60.4° till 58.2 °. Den 90:e percentil vinklarna (°)
för höger arm under hög belastning har också minskat från ett värde av 53.1° till 51.4°.
SUS-poängen på 75.6 indikerar en bra användbarhet för systemet.

3

Acknowledgments

First and foremost, I would like to thank my supervisor, Dr. Farhad Abtahi for all his help andguidance throughout this project.

I would like to express my gratitude to all the students at Hair Style Education who participatedin this study. This research would not have been possible without their help and cooperation.
Last but not least I would like to thank my family, especially my mother whose unconditional
love and support have always been my only source of strength and endurance in life. Words
cannot express how grateful I am for having you in my life.
In addition, I would like to thank Timm Schneider for all his love and support. Thank you for
proofreading my thesis and for your many constructive suggestions. I am forever thankful foryour presence in my life.

I would like to leave the remaining space in memory of Dennis Borgström, a brilliant young
scholar whose work I have referenced in my thesis.

4Table of Contents
1.Introduction ................................................................................................................................................. 1
2.Aims ............................................................................................................................................................. 2
3.Method and Materials................................................................................................................................. 3
3.1 measurement system ............................................................................................................................ 3
3.2 Field study setup .................................................................................................................................. 5
3.3 Feedback function ................................................................................................................................ 7
3.4 statistical data analysis ........................................................................................................................ 8
4.Results ......................................................................................................................................................... 9
4.1 General representation of data from 12 subjects ................................................................................ 9
4.2 Comparison of 90th percentiles (°) with and without feedback for 4 random subjects ................. 15
4.3 Comparison of data with and without feedback for one subject ..................................................... 16
4.4 System Usability Scale (SUS) Questionnaire.................................................................................... 19
5.Discussion.................................................................................................................................................. 21
6.Conclusion and Future Work ................................................................................................................... 23
Appendix....................................................................................................................................................... 24
A. State of the Art ..................................................................................................................................... 24
A.1 Introduction ....................................................................................................................................... 24
A.2 Quantification of Repetitive Work .................................................................................................... 25
A 2.1 Work-cycles ................................................................................................................................. 25
A 2.2 repetitiveness .............................................................................................................................. 25
A 2.3 Task Similarity ............................................................................................................................ 26
A 2.4 posture ......................................................................................................................................... 26
A.3 Anatomy of the Affected Areas.......................................................................................................... 27
A 3.1 Tendon, muscle and ligament..................................................................................................... 27
A 3.2 Human torso ............................................................................................................................... 27
A 3.3 Shoulder ......................................................................................................................................28
A.4 Risk Assessment Methods ....................................................................................................................30
A 4.1 Self-reports ......................................................................................................................................30
A 4.2 Observations ...................................................................................................................................30
A 4.3 Technical measurements ................................................................................................................30
A.5 Motion Capture (MC) Systems ............................................................................................................. 32
A 5.1 MicroElectroMechanical System ................................................................................................ 32
A 5.2 Inertia Measurement Unit Sensors ............................................................................................ 32
A 5.3 Degree of freedom (DOF) ........................................................................................................... 33
A 5.4 IMU with two sensor types ......................................................................................................... 33
A 5.5 IMU with three sensor types ...................................................................................................... 34
A 5.6 Sample rate .................................................................................................................................. 35
A 5.7 Euler angles limitation for motion tracking in 3D .................................................................... 35
References .................................................................................................................................................... 37
5

Introduction

1.Introduction
Upper extremity disorders (UEDs) have become a major problem in modern society. UEDs orin other terms repetitive strain injury (RSI), physical overuse syndrome or occupational
cervicobrachial disorder can be due to several facts. Occupational use of the upper limb, highjob demand and personal characteristics such as coping are a few to mention [1]. Some of theless-well defined UEDs are manifested in the patient by discomfort, pain or tingling in regions
such as neck, shoulders, arms, wrists, and hand. Other UEDs that have well-defined symptoms
and signs include tendonitis, carpal tunnel syndrome, osteoarthritis, vibration white finger, andthoracic outlet syndrome. However, many of the UEDs are non-specific thus making it very
difficult to obtain a specific diagnosis. Another issue is that Symptom manifestation can takeup to weeks, months or even years [2]. Besides the work-related issues caused for the workers,
UEDs also form a huge economic burden due to healthcare and sick leave costs [1]. According
to a new global study by the European Agency for Safety and Health at Work (EU-OSHA), thecost of poor occupational safety and health has been estimated to 3.3% of the European Union
GPD. That is €476 billion a year which could be saved with the right health strategies andpractices [3]. Therefore, it is crucial to identify the risks of developing UEDs in every work
environment and act at an early stage [2].
There are approximately 400 000 hair salons and 940 000 hairdressers in Europe. The European
Agency for Safety and Health at Work (EU-OSHA) has recognized serious occupational healthrisks associated with hairdressing tasks. It has been estimated that musculoskeletal disorders(MSDs) are five times more prevalent among hairdressers than other occupations [3]. Repetitiveand constant movements of various body parts or holding awkward positions for long periods
may result in MSD problems [3].
Common MSDs along hairdressers include lower back problems that are usually caused byprolonged standing, spinal twisting or bending. Sitting on stools with no leg or back support
can aggravate this problem. Shoulder problems occur due to prolonged holding of arms in
abduction (upper arm positioned out to the side) which is common during hair cutting andstyling. Occupational hazards linked with hairdressing tasks can lead to lower productivity, sickleaves and untimely exits from the occupation. Therefore, the improvement of working
conditions for this group should be a major priority [4].
This thesis is part of a bigger project at We@Work. They have developed an integrated solution
for promoting safety and health at work by combining wearable technology, ergonomics, Big
Data analytics and information and communication technologies. The aim of the project is toensure a healthy working environment through pervasive monitoring as well as enabling
accurate risk assessment acquisition and self-management of physical workload [5,6].
This study has been ethically approved by the Regional Ethics Committee in Stockholm (Dnr
2016/724-31/5).

1

Aims

2.Aims
The aim of this thesis is to investigate the feasibility of using a wearable system for minimizing
occupational injuries for hairdressers.
This project specifically aims to answer the following research questions:
Is there adverse biomechanical exposure associated with hairdressing tasks for the specificgroup chosen for this study?
Can audio feedback reduce adverse biomechanical exposure in hairdressing?
Is the system perceived as usable amongst hairdressing students?

2

Method and Material

3.Method and Materials
In this section, the measurement system and its components are introduced. The measurement
procedure is explained and information on which the feedback function has been based on isprovided.

3.1 measurement system
The set of the wearable system used in this study consisted of a sensorized t-shirt, 3 wirelessminiature Inertial Measurement Units (LPMS-B2, LP Research, Tokyo, Japan, size 39×39×8mm) and an Android smartphone (SAMSUNG SM-A520F). Three small pockets woven into
the Smart shirt housed the IMU sensors at the neck and upper shoulder areas (figure 1). The
accelerometer in the IMU had a range of±16 G with a 16-bite resolution and a sampling
frequency of up to 400 Hz. In this study, the sampling frequency was set to 30 Hz. The datafrom the IMUs were wirelessly transmitted through Bluetooth to an Android smartphone where
the data were stored and processed.
An android-compatible application was connected to the system via Bluetooth. It integrated agraphical user interface and a communication system that provided the users with both visualand auditory feedback. Data collection for post-analysis and real-time analysis of the data forproviding feedback were performed by the application simultaneously. The application could
run on smartphones with the operative system Android Marshmallow 6.0 or higher. The
smartwatch was not used during this study.

Figure 3.1. Measurement system with three IMU sensors

3

Method and Material

3.1.1 Inertia Measurement Units components
IMU sensors gather data on displacement and angular velocity using accelerometers,
gyroscopes, and magnetometers. The best way to explain the accelerometer in an IMU sensor
is to think of a system consisting of a spring that is attached to a weight called proof mass. The
spring constant and the weight of the proof mass are both known. Acceleration of the system
will cause the movement of the proof mass. MEMS accelerometers measure this movement
either by using capacitors (Figure 7a) or piezoresistors (Figure 7b). A tri-axial MEMS
accelerometer can calculate the tilt angles due to the fact that the force of gravity at sea level isstatic everywhere on the earth’s surface [7].

Figure 7a. Capacitive MEMS accelerometer

Figure 7b. Piezoresistive MEMS accelerometer

A gyroscope is a device that measures angular velocity. MEMS gyroscopes use vibrating
mechanical elements (proof mass) to sense rotation. All vibratory gyroscopes measure rotation
through Coriolis force. The Coriolis force is an inertial force that can be explained as follows:a stationary mass particle (m) with a distance R from the Centre of rotation in a rotating system(Ω) appears to be affected by an inertial force called the centrifugal force (C) for an observerthat is in a rotating frame of reference [8].
C=m (Ω×(Ω×R))(1)

If the particle moves with velocity Vr relative to the rotating system it will appear to be affectedby an additional inertial force called the Coriolis force (F) [8].
F= -2mΩ×Vr(2)

A MEMS gyroscope consists of a vibrating structure (proof mass) which can oscillate freely
with a certain frequency on the X-Y plane. The two modes of vibration are referred to as drivemode and sense-mode respectively. Measurement of the shift in the amplitude of proof massoscillation in the sense-mode, which is caused by the Coriolis force, is a direct measurement of
the angular rotation [9].

4Method and Material

Magnetometers are used to determine the orientation of the device with respect to its
environment. MEMS magnetometers exploit the Lorentz force principle for the detection of theexternal magnetic field. According to the Lorentz force principle, if current flows through a
conductor in the presence of a magnetic field, a force is generated. This force can be calculatedby the Lorenz force equation:
Florentz = (IL×B)
(3)
Where

I = I0 sinωt(4)

In the above equation, Florentz is the generated
force, I is the alternating current, L is the lengthof the conductor where current is flowing and B
is the applied magnetic field density [10]. MEMS
magnetometers are composed of structures thatare excited at their resonant frequencies. The
excitation source can be the Lorentz force that iscaused by the interaction of an external magnetic
Figure 8. Schematic view of the Lorentz force
field and excitation current.
This excitation causes a displacement in the resonant structure of the magnetometer, which canbe measured by optical, piezoresistive or capacitive techniques. The magnitude of thisdisplacement depends on the Lorentz force amplitude, which is directly proportional to theexternal magnetic field. Figure 8 shows a schematic view of the Lorentz force FL acting on analuminum loop with length Ly perpendicular to the magnetic field Bx. The force causes a
displacement of the loop at its midpoint [11].

3.2 Field study setup
This section contains information about the participants and the measurement procedure.
3.2.1 participants
Twelve hairdressing students were recruited (11 females and 1 male). Only students werechosen to partake in this study due to the assumption that they have not yet developed unhealthy
work habits and are able to change their posture with less difficulty. The subjects were between
21-44 years old.
3.2.2 Procedure
The measurements were conducted in a real work environment. Each participant was asked tosign a consent form and fill a pre-study questionnaire including demographic and physicaldiscomfort data (Appendix). Afterward, the participant was instructed to wear the shirt withsmall pockets for sensor placement. The IMUs for trunk and arms were turned on and placed
inside respective pockets. The name of the subject was entered in the profile section.
5

Method and Material

The sensors were connected. The subject was asked to stand straight as a reference posture andthe Smart shirt was calibrated.
When the participant was ready to start a haircut session the system was started. Eachparticipant was observed during data gathering and notes were taken regarding time points.
Each haircut session took about 1-2 hours and included tasks such as washing, cutting and blowdrying. Measurements were performed during two haircut sessions for each participant. The
first round of measurement was performed with no feedback from the system. The second roundof measurement provided the subject with auditory feedback on the position of the trunk andthe arms. The interval between each audio feedback was set to 300 seconds. The subjects wereasked to change their position in case they received a negative feedback on their posture. Aftereach session, the measurement was stopped, and the participant was asked to remove thesystem. After the second round of measurement, the participants were asked to fill a post-study
questionnaire based on a System Usability Scale (SUS) form. The data gathered from thisquestionnaire were analyzed to assess the overall usability of the system.

a

b

c

Figures 3.2 a, b and c. Screenshots of the android application interface

Figures 3.2 a, b and c are screenshots of the android application used in this study which
demonstrate the user interface for connection of the sensors to the application, calibration of
the sensors and data recording, respectively.

6

Method and Material

3.3 Feedback function
The feedback function of the system was based on the following values1.
Exposure variable

Risk limits

Category

SourceComments

Upper arm lifting angle

Flexion or abduction, > 90°,
<6% of working time

Green

RAMP II

Upper arm lifting angle

Flexion or abduction, > 90°,
>6% of working time

Yellow

RAMP II

Upper arm lifting angle

Flexion or abduction, > 90°,
>12.5% of working timeRed

RAMP II

Upper arm lifting
angular velocity

Average<60°/sangularvelocity

Green

HanssonUpper arm lifting
angular velocity

Average>60°/sangularvelocityRed

HanssonTrunk/back angle

Bending forwards <20°

Green

SES

Trunk/back angle

Bending forwards >20°

Yellow

SES

Static work posture
held >5s, otherwise
Green

Trunk/back angle

Bending forwards >45° or > 0°
bending backward

Red

SES

Static work posture
held >5s, otherwise
Green

Table 3.1 Recommended values for ergonomic risk assessment

Each color (red, yellow and green) indicates a different risk level of developing UEDs
associated with a task. Each risk level and the color associated with it is explained according to
RAMP II - In-depth analysis for assessment of physical risks for manual handling [12].
High risk. The loading situation has such a magnitude and characteristics that many
employees are at an increased risk of developing MSDs. Improvement measurements should
be given a high priority.
Risk. The loading situation has such a magnitude and characteristics that certain employees
are at an increased risk of developing MSDs. Improvement measures should be taken.
Low risk. The loading has such a magnitude and characteristics that most employees are at
low risk of developing MSDs. However, individuals with reduced physical activity may be atrisk. Individually tailored improvement measures may be needed [12].

1 1. RAMP II - In depth analysis for assessment of physical risks for manual handling, English version 1.00, 2014

2. Hansson, G.-Å., Arvidsson, I., Nordander, C. - Riktvärden för att bedöma risken för belastningsskador, baserade på tekniska mätningar
av exponeringen, Arbets- och miljömedicin, Lund, 2016, Rapport nr 4/2016
3. SES - Scania Ergonomic Standard for Design, Ergonomic Load Evaluation Manual, Issue 3, 2015-08-14

7

Method and Material

3.4 statistical data analysis
A paired sample t-test was performed on the data obtained from the measurements using IBM
SPSS software.

8

Results

4.Results
4.1 General representation of data from 12 subjects
Section 4.1 contains the data gathered from 12 participants without using the feedback function.
4.1.1 Time percentage of various exposure variables
Table 4.1 shows the exposure variables for all 12 subjects. The values represent the timepercentage of each exposure variable for one work-cycle which is defined as one haircut
session. Each work-cycle is then divided into three fundamental work-cycles, washing, cutting
and drying.
Exposure variable

Risk limits

12 subjects

Upper arm lifting angle (right arm)

Flexion or abduction > 90°

0.8% (6.9min)

Upper arm lifting angle (left arm)

Flexion or abduction, > 90°

0.6% (5.2min)

Trunk/back angle

Bending backwards >20°

0%

Trunk/back angle

Bending forwards > 45° or bending
backwards > 0°

53.2% (427.8min)

Table 4.1 Time percentage of different exposure variables during one work-cycle for all subjects (min stands forminutes)ab

Figure 4.1 a and b, demonstration of measured angles in Flexion and Abduction respectively

Data in table 4.1 represent the total measurements from 12 subjects recorded without using the
feedback function. Values in table 4.1 point a high value (53.2% of one work-cycle) for trunk’s
duration of bending forwards > 0°.

9

Results

4.1.2 10th and 90th percentiles of arms and trunk angular distribution
Figure 4.2 demonstrates the 10th and 90th percentiles (°) of the angular distribution for arms
and trunk during one work-cycle without feedback function for all 12 subjects.

2.6°

90th Percentile

2

56.6°
58.4°

-28.0°
1

8.5°

10th Percentile

-40

-30

-20

-10

8.1°

0

10

20

30

40

50

60

70

Angle(°)
Trunk

Right arm

Left arm

Figure 4.2 10th and 90th percentiles (°) of left arm, right arm and trunk (12 subjects)

The 90th percentiles of right and left arm angular distribution are 56.6° and 58.4° respectively,
showing a high value for 90 percent of the total data gathered from every 12 subjects during
one work-cycle. The 90th percentile of trunk shows that 90 percent of the data is below 2.6°.
4.1.3 Graphical representation of angular distribution for 12 subjects
Figures 4.2 and 4.3 demonstrate the angular distribution of arms and trunk during one workcycle.

Right arm

Frequency

Frequency

Left arm

Angle (Degrees)

Angle (Degrees)

Figure 4.2 Angular distribution of left and right arm for 12 subjects

10Results

Figures 4.2 and 4.3 demonstrate that the data approximately follows a normal distribution (redcurve represents the normal distribution). The normal distribution shows that 65 percent of thevalues lie between 10 to 50 degrees with a mean value of 30.30 for the left arm and 29.70 for
the right arm.

12 subjects

Mean(Degree) ±SD

Left-arm

30.3 ± 19.5

Right arm

29.7 ± 18.1

Trunk

-11.7 ± 13.3

Table 4.2 Mean (Degrees) ±SD for arms and trunk

Figure 4.3 Angular distribution of trunk

Figure 4.3 demonstrates the angular distribution for the trunk during one work-cycle without
the feedback function. negative values represent bending forwards from the upright position.
According to table 4.3, 65 percent of the values for trunk angles lie within 1° to -25°
approximately with a mean value of -11.7°.

4.1.4 Comparison of data with and without a load on forearms
Table 4.3 shows the time percentage of right and left arms elevation angles during afundamental work-cycle when there is a load on forearms (drying) and during the 2 otherfundamental work-cycles when there is no load on forearms. The data in the table represents
elevation angles > 60° for the fundamental work-cycles with no load on forearms and elevation
angles > 30° for the fundamental work-cycle with a load on forearms. The time percentage iswith respect to each fundamental work cycle duration and represents the percentage of time forwhich the mentioned elevation angles were held. The load (dryer) is between 1-2 Kg dependingon the dryer. No load and load conditions are defined as work elements in each fundamental
work-cycle. The time percentage in this table is with respect to the time it takes to perform eachwork- element. The values for strenuous work for shoulders demonstrated in this table are withrespect to values published by AMM (arbets och miljömedicinsk) in Lund2.

2 See table.2 in Appendix

11

Results

Strenuouswork forshoulders

Right arm elevation angle

left arm elevation angleTime(percent)

10th

90th

Mean(Degree)±SD

Time

10th

90th

Mean(Degree) ± SD

No load on
forearms(>600)

6.3%

9.1

54.5

30.0±18.2

8.7%

9.0

58.2

30.1± 19.5

Load onforearms(>300)

41.0%

8.4

53.1

28.9±17.8

43.3%

8.7

60.4

30.9 ±19.6

Table 4.3-exposure variables with and without a force on forearms (data from measurements with no feedback)

Values in Table 4.3 show that when there is a load on forearms, the arms are elevated morethan 30° for higher than 10 percent of the fundamental work-cycle duration. Elevation angles
higher than 60° when there is no load on forearms occur less than 10 percent of the work-cycle
duration. The rest of the data is similar for both work elements.

4.1.4 comparison of data with and without feedback during one fundamental workcycle during drying
A comparison of data for measurements with and without feedback is represented in table 4.4.
The data represents measurements performed during the drying fundamental work-cycle,
therefore, the arm abduction angles above 30° have been considered.
Arm abduction angle (degrees)>30°

Right-arm

Left-arm

No feedback

With feedback

No feedback

With feedback

Time percentage

41.0%

36.3%

43.3%

37.1%

10th

8.4

7.8

8.7

8.3

90th

53.1

51.4

60.4

58.2

Table 4.4 Comparison of data with and without feedback

The time percentage of arm elevation angles > 30° has decreased for both arms with the
feedback function. There is also a slight decrease in the 10th and 90th percentile values of botharms when the feedback function was used.

12

Results

The Mean angle for both right and left arm with and without using the feedback function as
well as the Standard Error Mean (SEM) are represented in table 4.5.
Paired Samples statistics

Mean (degree) ± SD

Standard. Error. Mean (SEM)

Left arm-no feedback

30.9 ±19.6

0.180

Left arm-with feedback

28.8 ±19.5

0.187

Right arm-no feedback

28.9±17.8

0.169

Right arm- with feedback

27.1 ±17.5

0.168

Table 4.5 Mean values (angles) for left and right hand with and without the feedback function

As it is seen from the values represented in table 4.5 the mean value of both left and right-handangles have decreased when the feedback function was used. SEM < 1 indicates a low variationin the sample data meaning the sampled data can be a good representative of the general
population.

4.1.5 Graphical comparison of data with and without feedback
Figure 4.4 demonstrates a graphical comparison of data with and without using the feedback
function during one fundamental work-cycle (drying).

aWith feedback

Angle (Degrees)

Angle (Degrees)

Without feedback

Frequency
Figure 4.4 a, Right arm comparison of elevation angles with and without feedback

13ResultsbWith feedback

Angle (Degrees)

Angle (Degrees)

Without feedback

Frequency
Figure 4.4 b, Left arm comparison of elevation angles with and without feedback

According to the figure, 4.4 lower elevation angles have similar frequencies for data with andwithout the feedback function. However, a decrease in frequency for elevation angles > 40° isseen for both arms when the feedback function was used.
Figure 4.5 a-c demonstrates some of the positions during one work-cycle.
abcFigure 4.5 a, b, c Demonstration of shoulders and trunk position during one work-cycle
14

Results

4.2 Comparison of 90th percentiles (°) with and without feedback for 4
random subjects
Figure 4.6 demonstrates a comparison between 90th percentiles of the left arm with andwithout using the feedback function for 4 participants chosen randomly (for one work-cycle).

Left arm
140

Angles (Degrees)

120
100

56.1

80

67.8

72.0

60

44.1

72.5
60.7

40

62.4

45.1
20
0
S1

S2

S3

Without feedback

With feedback

S4

Figure 4.6 90th Percentiles (°) of left arm for 4 random subjects (S stands for Subject)

According to figure 4.6, the 90th percentile value has decreased considerably for subject 1 when
the feedback function is used. Values for subject 3 are almost the same for both measurements
while subjects 2 and 4 show an increase of the 90th percentile when the feedback function is
used.
Figure 4.7 demonstrates the 90th percentiles of the right arm with and without using the feedback
function. The same pattern is seen for the left arm as well as for the right arm. Subject 1 showsa decrease in the 90th percentiles angles when the feedback function is used while S2 and S4
show an increase.

Right arm
140

Angles (Degrees)

120
100
80
60
40

50.7

77.7
40.7

57.7

64.6

50.1

44.8

48.8

S2

S3

S4

Without feedback

With feedback

20
0
S1

Figure 4.7 90th Percentiles (°) of right arm for 4 random subjects (S stands for Subject)
15

Results

4.3 Comparison of data with and without feedback for one subject
In this section, a comparison of data with and without using the feedback function is presented.

4.3.1 Time percentage of various exposure variables with and without feedback
function
Table 4.6 shows the time percentage of various exposure variables with respect to the durationof one work-cycle. The values represent measured data without and with feedback for one
subject. This data was chosen for this comparison since the length of both work-cycles wasapproximately the same with a difference of 16 minutes. Each work-cycle was divided into
three fundamental work-cycles namely washing, cutting and drying. The comparison of datawith and without feedback function was done for two arm abduction angles in Table 4.6. Values
for the angles above 300 represent the more demanding fundamental work cycle (drying) due
to a load on forearms (~1 kg) and holding the arms at high angles in extended periods. The
length of the drying fundamental work-cycles differs with only 2 minutes meaning 120 moredata points for the measurement with feedback.
The data in Table 4.6 demonstrates a comparison of measurements with and without feedback
for subject 6 with respect to recommended values by AMM (arbets och miljömedicinsk) in
Lund for strenuous work for shoulders.
Arm abduction angle (degrees)

Time percentage
elevation angleofRight-arm

Time percentage
elevation angleofleft-arm

No Feedback

Feedback

No Feedback

Feedback

No load on forearms (>600)

19.2% (7.3min)

3.8% (2.2min)

11.1%(4.2min)

7.6%(4.4min)

Load on forearms (>300)

63.0%(12.6min)

33.3% (7.3%)

52.2%(10.4min)

35.1%(7.7min)

Table 4.6 comparison of arm abduction angle for subject 6

Table 4.6 shows that the duration of holding a strenuous position has decreased in almost allcases when the feedback option was used. Duration of holding the left arm elevated more than
60° has increased with the feedback function even though the time percentage has decreased
which can be due to the inequality of the measurement data points.

4.3.2 Comparison of arms angular distribution with and without feedback function
Figure 4.5 a and b show a comparison of angular distribution for left and right arm respectively
with and without the feedback function. The fundamental work-cycle with a load on forearms
was chosen for this comparison.

16

Results

Left arm
Without feedback

With feedback

Angle (Degree)

Angle (Degree)

Frequency

Right arm
Without feedback

Angle (Degree)

Angle (Degree)

With feedback

Frequency
Figure 4.8 A comparison of arms elevation angles for data with and without feedback function

Figure 4.8 demonstrates an overall decrease in frequency for arm abduction angles above 500
approximately, when data was recorded while feedback was available to the participant
compared to data recorded without the feedback function. The distribution shift from higher
angles to lower angles with the feedback function indicates that the arms were held at lower
angles rather than higher angles (>50°), giving rise to an increased frequency for lower angles.

4.3.3 Statistics for data with and without feedback function
Table 4.7 contains the result of a paired sample statistics analysis performed on measurementswith and without feedback during one fundamental work-cycle (drying).

17

Results

Paired samples statistics

Mean (degree) ± SD

Std.Error Mean (SEM)

t-valuep-value

Left arm- no feedback

37.0° ± 24.33°

0.69

10.711

0.000

Left arm-with feedback

28.5° ±16.5°

0.45

Right arm- no feedback

38.4° ±18.8°

0.53
16.620

0.000

Right arm-with feedback

27.4° ±15.7°

0.43

Table 4.7 paired sample statistics for subject 6

Values in Table 4.7 indicate that the mean value of the angular distribution for both arms has
decreased when the feedback function was used. SEM < 1 indicates a low variation in thesample data meaning the sampled data can be a good representative of the general population.
T-values > 0 and p-values equal to 0.000 indicate a significant difference between the set ofangles measured with and without the feedback function
4.3.4 10th and 90th angular distribution percentiles
10th and 90th percentiles (°) for arm angular distribution during drying when there is a load on
forearms are demonstrated in figure 4.9.
80
70

74.8

60

62.6

Degrees (°)

50

49.4

48.1

40
30
20

14.0

10

11.6

12.9

11.4

0
10th percentile (Nofeedback)

10th percentile (Withfeedback)left arm

90th percentile (Nofeedback)

90th percentile (Withfeedback)

Right arm

Figure 4.9 10th and 90th percentiles (°) for arm angular distribution

Figure 4.9 shows that the 10th percentile (°) for the right arm has decreased from 14.0 to 11.4
and the 90th percentile has decreased from 62.6 to 48.1 when the feedback function was used.
There is also a decrease for the 90th percentile of the left arm angle from 74.8 to 49.4 with the
feedback function but an increase from 11.6 to 12.9 for the 10th percentile.
18

Results

4.4 System Usability Scale (SUS) Questionnaire
The SUS questionnaire is a reliable way to investigates the overall usability of a system. The
SUS numeric score determines the overall usability of the system from a scale of 0-100. The
SUS score for the system used for this study was calculated to 75.625. A graphic presentationof the result of the questionnaire filled by all 12 participants is demonstrated in figure 4.10. The
questionnaire contains the following questions:
1. I would use the device often
2. I experienced the device as too complex to use
3.

I think the device was easy to use

4. I need help from a technical person in order to be able to use the device
5. I think different parts of the device work coherently with each other6. I think the device was inconsistent
7. I think it is possible to learn how to use the device in a short period
8. I experienced the system difficult to use
9. I feel confident about how to use the device
10. I needed to learn a great deal before being able to use the device
10
9
8
7
6
5
4
3
2
1
0%

10%

20%

30%

Strongly Agree

40%
Agree

50%
Neutral60%
Disagree

70%

80%

90%

Strongly Disagree

Figure 4.10 Graphic presentation of system usability questionnaire (Likert scale).

19

100%

Results

Figure 4.10 demonstrates the Likert scale percentage for each question. As it is seen above,
40% of the participants would use the device often. However, 67% think that the device is toocomplex to use. 50% of the participants think they would need the help of a professional to beable to use the device. However, 75% think that it is possible to learn to use the device after ashort period of training and use.

20

Discussion

5.Discussion
Lifting unsupported arms decrease the blood supply of supraspinatus already at 30° abduction.
The blood supply ceases completely at 60° abduction. This indicates a risk of developing
Musculoskeletal Disorders (MSDs) especially if the positions are held in long periods and withno rest in between [13]. RAMP ‐ Risk Assessment and Management tool for manual handling
Proactively, has three risk and priority levels which are as follows [12]:High risk. The loading situation has such a magnitude and characteristics that many
employees are at an increased risk of developing MSDs. Improvement measurements should
be given a high priority.
Risk. The loading situation has such a magnitude and characteristics that certain employees
are at an increased risk of developing MSDs. Improvement measures should be taken.
Low risk. The loading has such a magnitude and characteristics that most employees are at
low risk of developing MSDs. However, individuals with reduced physical activity may be atrisk. Individually tailored improvement measures may be needed.
Comparison of values in table 4.1 with the values recommended by the RAMP study (section
1.4 back postures) suggests that the back/trunk posture (bending forwards > 45° or bending
backward> 0°) falls in the high-risk category and improvements should be a high priority. Itshould be mentioned that the measurements belong to one work-cycle. However, the values
increase for one 8-hour working day. Each subject has about 2 to 3 haircuts per day whichmeans that the values increase by a factor of 2 to 3 approximately for one full working day.
RAMP also suggests values for risk assessment of work-elements while there is a load on
forearms. According to section 3. Lifting work in RAMP, the drying work-cycle falls in thehigh-risk category with the weight and frequency factor of 0.6 (weight of approx. 1-2 kg andfrequency between 13-24). The lifting area factor is 2.8 plus the 1.7 factor for lifting the loadwith only one hand. The overall score is 5.1 > 5 which is the recommended value for high-risk
lifting tasks by RAMP.
Table 4.3 shows a high time percentage (>50% of the work cycle length) for elevation angles>30° during the drying work-cycle which indicates that the arms were elevated >30° for morethan half of the fundamental work-cycle duration. According to AMM (arbets och
miljömedicinsk) in Lund these values indicate a risk for developing MSDs. The 10th and 90th
percentiles are similar for both work-elements; however, the angles were held while there wasa load on forearms during the drying fundamental work cycle which adds to the overall risk
score of the task for developing MSDs according to the RAMP study. Therefore, this study has
mostly focused on analyzing the data for the drying fundamental work-cycle.

21

Discussion

Table 4.4 shows that the angular distribution average for arms have decreased when the
feedback function was made available to the participants. The 10th and 90th percentiles havealso decreased with the feedback function. However, the values are very close to each other.
This can be due to the individual contribution of each participant to the total data. A comparisonof the exposure variables values for measurements with and without the feedback function for
individual participants shows various results. In some cases, the values such as 10th and 90th
percentiles have decreased when the feedback function was used and for other cases, it has notchanged significantly or has even increased. This variation can be due to several factors suchas an individual’s knowledge of ergonomics and how to change their posture when they receive
negative feedback from the system.

A better comparison of data with and without the feedback function is possible when looking
at the data individually. Measurements from one of the participants were chosen for thiscomparison. Figure 4.5 a and b show a shift in angular distribution from higher degrees to lower
degrees (<40°) for both arms when the feedback function was used. This indicates that theparticipant has worked with lower arm angles during the measurement with feedback comparedto the measurement without feedback which has given rise to an increased frequency for lower
angles.
Values in Table 4.7 also agree with the graphic presentation of the angular distribution in figure4.5. It is indicated that the mean angular distribution for both arms has decreased when the
feedback function was available to the participant. The results from paired t-tests performed oneach arm measurement with and without the feedback function indicate a significant difference
between each data set. T-value > 1 and p-value < 0.5 indicate that the null hypothesis can berejected which means that there is a significant difference between the dataset recorded during
the feedback function compared to the dataset recorded without the feedback function.
Table 4.7 indicates that for all cases except left arm 10th percentile, the 10th and 90th percentiles(°) have decreased significantly when the feedback function was used. Decreased percentile
values show that the arms elevation angles were lower when the participant received feedbackon their posture meaning the feedback function was efficient in making the participant awareof their posture.

5.4 Interpreting System Usability Scale (SUS) Score
The average SUS score is 68 which means the system is at the 50th percentile. A score between
68-80.3 gives the adjective rating Good (B) for the system.3 A system with a score of 75.625
is at the 73th percentile meaning it has higher perceived usability than 73% of all products tested
with this system.

3 https://uiuxtrend.com/measuring-system-usability-scale-sus

22Conclusion and Future Work

6.Conclusion and Future WorkThe result of this study agrees with the previous studies presenting the hairdresser profession
as a high-risk profession for developing UEDs. This means that hairdressers are at a high riskof developing occupational UEDs such as tendinitis, osteoarthritis and carpal tunnel syndrome
to name a few. Therefore, there is a need for a system that provides objective data for risk
assessment of hairdressing tasks. This data could be useful for optimizing the ergonomic designof the workplace.
The variation of data amongst different subjects suggests that the feasibility of the feedback
function is enormously dependent on the individual’s knowledge of ergonomics and willingness
to change work habits. Therefore, it is suggested that the device be used together with providing
educational programs on ergonomics at work and the consequences of unhealthy work habits.
The ergonomic design of the workplace also plays an important role since it becomes very
difficult to change a posture if the workplace is not ergonomically optimized. The height of theparticipant and the customer plays an important role in arm elevation angles. In some cases, thechairs had to be put in a higher or lower position. However, this was not possible with the chairs
available at this specific hair salon.
For future studies, it could be effective to increase the number of measurements in order tocompensate for the variability of work cycle duration by increasing the amount of sampled data.
Ideally, the length of both datasets should be the same for a more accurate comparison ofindividual data. However, this is not possible for the hairdresser profession since the costumer
determines many of the working conditions.
The participants in this study were all students. Their lack of experience, their need to focus
extra on each task and having to communicate with their supervisor while working made it extra
difficult for them to focus on the feedback given to them by the device. The participant that wasmore experienced (section 4.3) was able to focus more on the feedback and change her posturemore easily. Therefore, it would be interesting to repeat the measurements with professional
hairdressers since the result of the effectivity of the feedback function could change
considerably.

23

Introduction

Appendix

A. State of the Art

A.1 Introduction

According to the sixth European Working Conditions Survey (EWCS) in 2015, 44.4% ofworkers had reported suffering from muscular pain in the shoulders, neck or upper limb in thelast 12 months [2]. The Swedish Work Environment Authority (2008) reported that 14.8% offemale hairdressers in Sweden reported occupational UEDs in the past 12 months [14].
However, since the surveys are mostly based on self-reported health problems it is difficult toestimate the true extent of the problem. Table 1 < indicates the type of work-related issues andtheir prevalence amongst different professions reported by the European Union [2].
Type of work-related health problem

Workers (%)

Muscle problems affecting the back region

29.5

Neck, shoulders, arms or hands

20.1

Hips, legs or feet

11.3

Table 1. Type of work-related health problem indicated as the most serious among persons with a work-related healthproblem in the EU27 (%) [2]
Many kinds of research have been carried on evaluating the ergonomic conditions in salons.
However, most of them rely on observational studies and surveys as a means of data collection.
For example, according to a study conducted by students at the University of Central Florida
cosmetologists are at moderate risk of developing injuries over a long-term period [15]. In
another study based on self-reported surveys, it is concluded that hairdressers are at no highrisk of developing MSDs due to occupational circumstances [16]. However, according to theEuropean Agency for Safety and Health, cosmetologists are exposed to serious occupational
health risks that can lead to developing MSDs especially in neck, shoulders and back regions[17]. The inconsistency in the data collected by different research groups is an indication of aneed for a more objective system. This study aims to evaluate the feasibility of using ameasurement system that has been developed to quantify the ergonomic risk assessment process
for different occupations. This quantification allows for obtaining a more concrete and objective
data which could lead to a more accurate and effective problem-solving.

24Quantification of Repetitive Work

A.2 Quantification of Repetitive Work
Ergonomics is the science of designing work environment and equipment with regards to theworker’s health and safety. “Ergonomic factors” is the term referring to situations where there
is a misfit between the working environment and worker’s health. Ignoring ergonomic factors
in the work environment design can lead to physical and mental exhaustion for the workers.
These issues usually arise from constant and repetitive work or holding prolonged incorrect
positions [4]. Repetitive work can be defined as similar physical work that is repeated under aspecific period of time. In order to quantify this definition, repetitive work should be specified
with regards to the duration and frequency of work cycles. For the prevention of
musculoskeletal disorder studies, it is desirable to include parameters such as external force,
postures and static load. According to biomechanical characteristics repetitive work can beclassified into two categories, intermittent static or dynamic. However intermittent static
movements should be distinguished from static movements. Static movements differ from thetwo movement categories mentioned above in the muscle activation between contractions. In
such movements, there are no muscle breaks (or micropauses) in between contractions. Thereis no minimum duration available for a movement to be categorized as static. However, in moststudies contractions exceeding 30-60 seconds are accounted as static movements [18].

A 2.1 Work-cycles

A cycle of work is the duration in which a task is performed. This cycle is subdivided into
fundamental work cycles. Each fundamental work cycle is composed of work elements. Eachwork element puts a demand on a different region of the body involved in the task performing.
For example, in the case of packing a box of fruit, packing the box defines the cycle of work.
Packing each fruit is the fundamental work cycle. Grabbing, holding and wrapping the fruits
are the work elements in the fundamental work cycle. Identifying the tolerance for each work
element with regard to the risk of fatigue can be effective in designing prevention plans formusculoskeletal injuries [18].

A 2.2 repetitiveness

There are different definitions available for repetitive work. However, these definitions aremostly very close to each other in quantifying repetitive work. A few of these definitions are
mentioned below.
In a study in Ergonomics considerations in hand and wrist tendinitis, high-repetitive tasks were
defined as tasks with a time-cycle of less than 30 seconds or tasks with the25Quantification of Repetitive Worksame motion pattern for more than 50% of the whole cycle time. Jobs with an average handforce requirement of 40 N were considered as high-force jobs and those with an estimatedaverage of 10 N were considered as low-force jobs [19]. Another study defines repetitive work
as work elements with more than 15 times of repetition per minute and engaging less than 1/7of the muscle mass [18]. A study in Ergonomic Design for People at Work defines highly
repetitive work as tasks with cycle times below 30 seconds [20].

A 2.3 Task Similarity
Motor actions can be similar in three aspects:-

Time, i.e. similarity in the duration of work cycles
Space, i.e. similarity in the spatial pattern of work cycles
Force, i.e. similarity in force exertion over time [21]A 2.4 posture
Musculoskeletal disorders due to repetitive work can depend highly on task performance
posture [21]. According to the American College of Orthopedic Surgery, postures for different
joints can be defined by the degree of deference from the neutral position of the joint. The
degree deference can be measured using goniometers during the task performance [22]. It has
also been found that the posture duration of a performed task is likely to have an influence on
the risk of developing musculoskeletal disorders due to repetitive work [23]. According toseveral studies with access to technical instrumentation, repetitiveness and posture of different
movements can be assessed by spectral analysis of goniometer recordings. These recordings
provide information on the speed and acceleration of the movements [21].

26Anatomy of the Affected Areas

A.3 Anatomy of the Affected Areas
A 3.1 Tendon, muscle and ligament
Tendons are connective tissues that connect muscle to bone. Static or prolonged movements
can cause stress that is beyond the strength of the tendon. This stress will lead to small tears in
the tendon. Another issue is inflammation of the tendons which is caused by friction due tooveruse. Tendons in the hand and wrist are at a higher risk of injury due to overuse because of
their smaller structure when compared to other tendons in the upper extremity. This conditionis called tendinitis which is most common for tendons of the fingers, thumb, forearm, elbow,
and shoulder. Tendinitis in the shoulder area can occur if the arms are held out in front or off tothe side for long periods. Symptoms include pain, stiffness, tightness and burning sensation in
the affected area [24].
Muscle injury which is caused by overuse leads to tissue scarring, inflammation, and stiffness
in the affected muscle. The result can be the formation of trigger spots in the overused muscle.
The pain from these spots is called referral pain due to the fact that the pain travels from thepoint of the trigger to a distant area and then recedes. Muscles in the neck refer pain to theshoulders, upper back, and hand. Muscles in the arms can refer pain to the neck, shoulders,wrists, and hands [24].
Ligaments are connective tissues that connect bones. Ligaments can also be injured by static,
prolonged postures. This can cause inflammation of the ligaments especially for the cervicalspine ligaments that are more susceptible to strain [24].

A 3.2 Human torso
The human torso is the central platform of the human body from which neck and limbs extend.
Its anatomy consists of a skeleton with many muscle pairs, ligaments, and nerves with different
functions [25]. The skeleton in the human torso consists of the cervical spine (C1-C7), thoracic
spine (T1-T12) Lumbar spine (L1-L5), 4 sacrum vertebrae and 4 coccyx. Torso rotation isenabled by axial rotation between vertebrae pairs. An intervertebral disk between each twovertebrae pair reduces friction between the pairs and functions as a shock absorber. Degenerated
disks have an increased risk of rupture. They also cause the vertebrae to be placed closer toeach other, which increases the risk of nerve compression. This is one of the common causesof thoracic spine pain which can be due to incorrect body posture, aging or disease [26]. Anothercommon cause of thoracic spine pain is Muscle spasm or strain caused by poor posture such as
forward head bending [25].

27Anatomy of the Affected Areas

Movement of the human body is usually modeled using three imaginary planes: the sagittal
plane, coronal plane, and transverse plane. Sagittal plane (anteroposterior) divides the bodyvertically into right and left halves. The coronal plane (frontal) divides the body vertically into
the front and back. The transverse plane (horizontal) divides the body horizontally into top andbottom halves of equal mass [27]. In the anatomical reference position, all body segments are
positioned at 0 degrees. Rotation of a body segment away from the reference position is namedby the direction of motion and the angle between the body segment’s position and the reference
position. Figure 1 illustrates the motion ranges of the human torso on three body planes [27].
Combined rotation around two or more axes of the torso increases the risk of attaining workrelated injuries [26].

Figure 1. Human torso rotation on three body planes

A 3.3 Shoulder
The movements of the human shoulder are
possible by a complex and dynamic relationshipbetween many muscle forces, bonyarticulations, and ligament constraints. Figure 2
illustrates the shoulder’s range of motion inthree body planes.
The shoulder joint has the most extensive rangeof motion in the body which is due to static anddynamic stabilizers. Injury of these stabilizers
either by trauma or overuse will increase therisk of shoulder related injuries significantly. A
different group of stabilizers is responsible forjoint stabilizing in the shoulder region
depending on the degree of abduction [28].
Figure 2. Shoulder range of motion28Anatomy of the Affected Areas

Repetitive arm motions in a certain direction can lead to the injury of different groups ofstabilizers [21]. The table below shows the recommended values for risk assessment ofdeveloping occupational musculoskeletal disorders in the upper arms. According to AMM(arbets och miljömedicinsk) in Lund, a work is counted as strenuous for upperarms if it surpasses the following values [13]:Strenuous work for shoulders
No load on forearms

Arm elevation angle(degrees)Time(% of a workday time)

>60

10

Load on forearms

>30

>= 50

Average movement speed ofshoulders
Recovery time (neutral posture)>60/seconds<5

Table 2. Recommended values by AMM indicating strenuous positions for shoulders

29

Risk Assessment Methods

A.4 Risk Assessment Methods
In general, there are three ways of obtaining risk assessment for different exposure variables.
They include Self-reports, observations and technical measurements [29].

A 4.1 Self-reports
Self-reports of workload exposure are very common in ergonomics research. The data is either
reported by the workers themselves or by the employers and group leaders. Self-reported data
is obtained through different methods such as using questionnaires, diaries, rating-scales and/orinterviews [29]. There are advantages and disadvantages of using self-reports as a means ofergonomics risk assessment. The advantages include easiness of data gathering from a large
study population, more economical compared to other methods and low participant burden [29,30]. The disadvantages include low precision, the requirement of a large study population inorder to increase the precision and low reliability [31, 29]. Studies show that the correlation
between self-reports and technical measurements is low to moderate with a mean value of 0.37
± 0.25 [32]. Another issue with self-reports could be the fact that people with pain tend to rate
their exposures higher than people without pain for the same workload [31].

A 4.2 Observations
For observational studies usually, a trained person will make observations by using checklists
and video recording [29]. When it comes to ergonomics studies using observation, there is asimilarity in reported data for exposure of large-scale body movements. However, for small andquick movements such as trunk rotation, the reliability for reported data is low [33].

A 4.3 Technical measurements
There are several types of technical measurements method for ergonomic risk assessment suchas optical capture system, sonic system, goniometric system and accelerometer system [34].
However, only a minor part of ergonomics studies has been conducted using technicalmeasurements. This could be due to the high economic burden of these measurements withrespect to both time and cost [32]. Other limitations of this method include having to learn how
to use the device, analyze the data and also the requirement of different software depending on
the type of study [29].

30Risk Assessment Methods

A 4.3.1 Current technical measurements devices
Integrated sensors in clothing have been used before for physiological signals and movement
pattern recording. Wearable technology has existed in healthcare for over a decade now. Efforts
by the European Union have led to remarkable research development and commercial attempts.
However, only a few products have been successful in reaching the mainstream market [6].
Table 2 contains some examples of research projects and commercial products in wearable
technology for healthcare funded by the European Union.
European research projects:








Bio-sensing textiles so support health management [35]Compliance and effectiveness in HF and CHD closed-loop management and HeartCycle[36]
Contactless sensors for body monitoring incorporated in textiles [37]Medical remote monitoring of clothes [38]MyHeart [39]OFSETH Optical fiber sensors embedded into technical textile for healthcare
monitoring [40]Protection e-Textiles: micro nanostructured fiber systems for emergency disaster wear
[41]

Commercial products:Hexoskin smart shirt [42]Nuubo. nECG shirt L1 [43]Polar Electro Oy. Polar H10 Hear Rate Sensor [44]Vivonoetics. The Equivital TnR Product Range [45]the wearable sensors market has experienced a standstill mostly due to lack of an actual
application that is able to pull the technology through and also the pricing of textile-electronic
manufacturing. However recent developments in preventive and personalized healthmonitoring systems support expansion for smart clothing and wearable sensors’ market [6].

31Motion Capture (MC) Systems

A.5 Motion Capture (MC) Systems
Most risk assessments of work-related musculoskeletal disorders (WMSDs) today are based on
self-reporting the issues. This data usually does not correlate well with actual data [31].
Quantifying WMSDs is the first step towards an objective risk assessment, which is essential
in the implementation of preventive plans and policies. This quantification can be done using
Motion Capture (MC) systems [26].
There are many different types of motion capture systems available today. However, they are
all similar in one aspect. They capture the movements and rotation of a rigid body. One methodof MC is to use Inertia Measurement Unit (IMU) sensors. The benefits of using IMU sensors
compared to other MC systems include low weight, small size, potential to be wireless orwaterproof and a considerably lower cost compared to other MC systems. Even though theaccuracy is not the highest compared to other MC systems, IMUs are most beneficial for theirmobility. The small size and possibility to make them wireless make it possible to wear thesensors by either sewing them into clothes or taping them to the skin [26].

A 5.1 MicroElectroMechanical System
IMU sensors were first used in 1930 for aircraft navigation and in large devices. They werelarge in size and had a high cost and power consumption. The introduction of the microelectromechanical system (MEMS) has made it possible to make compact, low cost and low
processing IMU sensors [46]. Components in MEMS have a size between 1-100 µm. The small
size of the components makes it possible to use materials that would be very costly in larger
size sensors. Another benefit of MEMS is its robustness and energy efficiency, which is also
due to the small size of the components. The cons of MEMS technology would be the high costof MEMS building devices and the requirement of fabrication in cleanroom laboratories. Most
dust particles have a size between 0.1-1000 µm. Therefore, they can potentially disturb sensors
function if present during fabrication [26].

A 5.2 Inertia Measurement Unit Sensors
IMU sensors are widely applied in motion tracking devices today due to their small size, lowweight and the ability to communicate wirelessly e.g. via Bluetooth. Data logging capability
and sufficiently low uncertainty have made IMU sensors an attractive measurement techniquefor human movements [26]. They determine movement in terms of angular velocity,
acceleration, and rotation. There are two types of IMU sensors. Those with two sensor types,
namely accelerometer, and gyroscope.

32Motion Capture (MC) Systems

Those with three sensor types namely accelerometer, gyroscope, and magnetometer [46]. The
integration of the gyroscope measurements determines the orientation of the sensors. Double
integration of the accelerometer measurements subtracted of the earth’s gravity, provides
information
about the position of the sensors. The subtraction of the earth’s gravity requires the orientationof the sensors to be known. The process of attaining information about the orientation andposition of sensors by integrating the measurements from the sensors is called dead-reckoning.
Figure 3 illustrates the process [47].
angularvelocity∫orientation

external
specificforce
rotateremovegravityacceleration∫∫

position∫

Figure 3. Dead-reckoning process

A 5.3 Degree of freedom (DOF)

The degree of freedom determines the number of independent parameters in a system. IMU
sensors that are available in the market today vary from 2-9 DOF. The number of DOF depends
on the type of sensors used in the IMU sensor and the number of the axis that it measures. In
position tracking systems, 6-DOF for two-sensor type IMU or 9-DOF for three-sensor type
IMU is usually used. The number of DOF represents the measurement in x, y, and z-axis foreach sensor. The accuracy of the sampled data increases with a higher number of DOF [46].

A 5.4 IMU with two sensor types

This type of IMU consists of accelerometer and gyroscope sensors. Each sensor has between
two to three DOF defined for x, y, and z-axis which will provide a total of four to six DOF.
The information from each sensor is stored separately. Angles are measured from both sensors
and the data is calibrated as shown in Fig. 4 in order to increase the accuracy of the output data
[46]
33

Motion Capture (MC) Systems

Figure 4. IMU with two sensors

A 5.5 IMU with three sensor types

This IMU with an accelerometer, magnetometer, and gyroscope, all in tri-axel commonly,
provides measurements in nine DOF. Measurement of yaw angle rotation by the magnetometer
provides the possibility to improve drift issues by calibrating this data to the gyroscope data[46]. Yaw, pitch, and roll (figure 6) are three rotation angles introduced by Euler that represent
rotations of a rigid body in ℝ3 [26]. Figure 5 illustrates the basic principle of a three-sensor type
IMU [46].

Figure 5. IMU with three sensors

Figure 6. Angles of rotation

34

Motion Capture (MC) Systems

A 5.6 Sample rate

The sample rate is the number of sampled data per second. Higher sample rates provide better
representations of the motion capturing. However high sample rates require increased
processing power due to a large amount of data. Therefore, an average sample rate which isboth a close representation of the movements and requires less processing power is preferred.
25 Hz is the sampling rate which is mostly used today [26].

A 5.7 Euler angles limitation for motion tracking in 3D
As mentioned before Euler angles use three sequential rotation
angles (yaw, pitch, and roll) to describe rotations of a rigid bodyin ℝ3 with respect to a fixed coordinate system.
Euler angles work well for small rotation angles but suffer fromsingularities when rotation angles attain larger values [26].
Matrices (1-3) define the three Euler rotation angles about X, Y
and Z-axes.
Figure 9. Euler angles geometricaldefinition (N is the line of nodes)

1
X (ψ) = 00
Y (θ) =

0
cos
−sin
0
1
0

0

0
sin
cos
−
0

Z (φ) = −
0

(1)

sin

(2)
0
0
1

(3)

The product of the three rotation matrices is equal to the transformation matrix R.
The order of the rotation axes affects the result because matrix multiplication is notcommutative [48]. In this text, the rotation is about the fixed axes z, y and x, RXYZ = X (ψ) Y(θ) Z (φ) (in that order).
RXYZ=
cos( θ) cos( φ)sin( ψ) sin( θ) cos( φ) − cos ( ψ) sin( φ)sin( ψ) sin( φ) + cos( ψ) cos( φ) cos( θ)cos( θ) sin( φ)cos( ψ) cos( φ) + sin( ψ) sin( θ) sin( φ)cos( ψ) sin( θ) sin( φ) − cos ( φ) sin ( ψ)35sin( θ)cos( θ) sin( ψ)cos( ψ) cos( θ)(4)Motion Capture (MC) Systems

Equation (4) represents the general form of the rotation matrix. Due to equations (5-7), there
are singularities in the transformation matrix (8) at angles θ = ± , ±

R=

(5)

sin ψ=( )(6)

sin φ =

…

( )

(7)

0
0
±1
±
sin(ψ)cos(φ)−
cos(ψ)sin(φ)
cos(ψ)cos(φ)±
sin(ψ)
sin
(φ)
0 (8)
RXYZ =sin(ψ) sin(φ)± cos(ψ) sin(φ) − cos(φ) sin (ψ) 0

Motion tracking of human movements requires tracking in all directions. The singularities inEuler angles are not suitable for such applications since they give rise to an issue called thegimbal lock [26]. When using three concentric frames for motion tracking a degree of freedom
is lost in certain positions. However, a minimum of three rotation angles must be combined torepresent an angular displacement. The loss of a degree of freedom gives rise to the gimbal lockissue [49]. Euler angles are suitable for representing motions of a rigid body in ℝ3 because they
are intuitive and are more suitable for analysis and control [50]. However, to correct for the
gimbal lock issue in angular displacement measurement for IMU sensors often the quaternions
coordinate system is used. A Quaternion, Q = q0 + q1+q2 + q3 = ∑is a four-element vector
composed of one real element and three complex elements. A quaternion coordinate system candescribe any rotation in ℝ3 [26]. The quaternions coordinate system and Euler’s angles can beused interchangeably for IMU sensors [47].

36References

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