Musculoskeletal disorders are a burden among the workers. This phenomenon does not spare the hospital environment which is subject to numerous constraints. To prevent musculoskeletal disorders, different physical ergonomic assessment methods exist and some of them have the particularity to combine observational methods with direct measurement tools. Some examples of the latter case have already been applied to the hospital field but studies are limited to surgeons and nurses. In our study, we performed physical ergonomic assessments on hospital staff specialized in disinfection tasks and patient displacements by combining the rapid upper limb assessment with inertial measurement units and video recordings. We intended to identify which upper body areas were most subject to hazardous postures and which subtasks were the riskiest ones by comparing results with literature but also pain participants complain about through a self-administered questionnaire, Nordic musculoskeletal questionnaire. Although the method we have developed was operational, our results on riskiest upper body parts have appeared to belie literature but also Nordic musculoskeletal answers. Plus, results on riskiest subtasks have not been confirmed by literature. In the future, the method should be tested on a higher number of participants and with more efficient data collecting and processing materials.

1. SORBONNE UNIVERSITÉ - MECHATRONICS SYSTEMS
FOR REHABILITATION
COMPLEXITÉ, INNOVATION, ACTIVITÉS MOTRICES ET
SPORTIVES - CIAMS
Graduation Thesis
Physical ergonomic analysis with embedded
sensors to prevent musculoskeletal disorders
among hospital staff
Author:
Daniel KOSKAS
Master’s supervisors:
Gérard SOU
Ludovic SAINT-BAUZEL
Internship supervisor:
Nicolas VIGNAIS
Academic year 2020-2021

3. Abstract
Musculoskeletal disorders are a burden among the workers. This phenomenon does not spare the
hospital environment which is subject to numerous constraints. To prevent musculoskeletal disorders,
different physical ergonomic assessment methods exist and some of them have the particularity to
combine observational methods with direct measurement tools. Some examples of the latter case
have already been applied to the hospital field but studies are limited to surgeons and nurses.
In our study, we performed physical ergonomic assessments on hospital staff specialized in dis-
infection tasks and patient displacements by combining the rapid upper limb assessment with inertial
measurement units and video recordings. We intended to identify which upper body areas were most
subject to hazardous postures and which subtasks were the riskiest ones by comparing results with
literature but also pain participants complain about through a self-administered questionnaire, Nordic
musculoskeletal questionnaire.
Although the method we have developed was operational, our results on riskiest upper body parts
have appeared to belie literature but also Nordic musculoskeletal answers. Plus, results on riskiest
subtasks have not been confirmed by literature. In the future, the method should be tested on a higher
number of participants and with more efficient data collecting and processing materials.
Keywords: Hospital staff; IMUs; MSDs; Nordic musculoskeletal disorders; Physical ergonomic
assessment; Risk factors; RULA.

4. Acknowledgments
First of all, I would like to thank Mr. Nicolas Vignais, associate professor at the CIAMS labora-
tory, for allowing me to participate in his research project as part of my final year internship. I am also
grateful for the trust he gave and for his guidance in the progress of my work, especially the writing
of this graduation thesis.
Then, I wish to express my thanks to the participants of the study and their colleagues, Pitié-
Salpêtrière hospital staff, who welcomed us well during these two days of field experimentation. I
also want to express these thanks to those who helped us to get in touch with the staff, Pr. Fabien
Koskas, Mr. Hervé Guyaux and Mrs. Sylvie Girard.
I want to thank Ulysse Merrheim and Simon Moutier, first-year interns, for the help they gave on
my work.
I also want to thank PhD students and other interns with whom I shared the office, lunch and
coffee breaks for five months and who gave me good advice all along the internship.
Finally, I would like to thank Mr. Gérard Sou and Mr Ludovic Saint-Bauzel who supervised my
master’s degree for two years.
1

5. Contents
1 Introduction 1
2 Theoretical framework 3
2.1 Musculoskeletal disorders among hospital staff . . . . . . . . . . . . . . . . . . . . 3
2.1.1 Definition and causes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.2 Musculoskeletal disorders detected among hospital staff . . . . . . . . . . . 4
2.2 Physical ergonomic assessment methods to prevent musculoskeletal disorders . . . . 6
2.2.1 Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2.2 Self-reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2.3 Observational methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.4 Direct measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 Combining observational methods and direct measurements for physical ergonomic
assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.1 General case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3.2 Among hospital staff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Goals and Assumptions 18
4 Materials and Methods 20
4.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2.1 Data acquisition materials: XSens inertial measurement units system and
video recordings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.2.2 Rapid upper limb assessment method for ergonomic analysis . . . . . . . . . 22
4.2.3 Nordic musculoskeletal questionnaire . . . . . . . . . . . . . . . . . . . . . 23
4.3 Experimental procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.4 Data acquisition and processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2

6. 4.4.1 Ergonomic scores computing . . . . . . . . . . . . . . . . . . . . . . . . . . 25
4.4.2 Subtasks segmentation based on video processing . . . . . . . . . . . . . . . 26
4.4.3 Features extraction for each subtask of each subject . . . . . . . . . . . . . . 26
4.4.4 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
5 Results 29
5.1 Global RULA scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.2 Local RULA scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.3 Subtask analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
5.4 Nordic musculoskeletal questionnaire answers . . . . . . . . . . . . . . . . . . . . . 36
6 Discussion 39
6.1 Main results compared to literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.2 Connection between local scores and musculoskeletal disorders . . . . . . . . . . . . 40
6.3 Subtasks compared to literature and connection with musculoskeletal disorders among
hospital staff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
6.4 Feedback comfort questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
6.5 Limitations and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
7 Conclusion 45
Bibliography 46
List of Figures 51
List of Tables 52
Appendix 54

8. Chapter 1
Introduction
The covid-19 crisis has brought to light the involvement of hospital staff and the difficulty of their
work. These workers indeed face numerous constraints, particularly physical ones, since they are
asked to perform tasks that are more and more demanding. These constraints contribute to various
risk factors of musculoskeletal disorders [Carneiro et al., 2019].
Occupational diseases mean all health damages that progressively occur among workers after a la-
tency period in the course of their work. Most of them are musculoskeletal disorders [Garoche, 2016].
The latter are due to various factors such as gesture repetitiveness, force exertion, temporal pressure,
awkward postures, inadequate equipment... and they generate socio-economic consequences at both
the individual and corporate levels. In fact, while consequences at the individual level are functional
disabilities in the worker, those at the corporate levels are direct and indirect costs. The former are,
for example, financial compensation and the latter can be decrease in productivity due to absence or
limitation of the worker [Aptel et al., 2011]. In 2017, direct costs were worth two billions of euros for
French companies [AME, 2020].
Musculoskeletal disorders can be prevented by identifying their risk factors through physical er-
gonomic assessment. Different methods and tools exist and they can be classified in three main fami-
lies: self-reports, observational methods and direct measurements [David, 2005] [Li and Buckle, 1999].
In the last years, combining observational methods with direct measurement tools have appeared to
be useful for continuous assessment in real conditions. A relevant example is the combination of the
rapid upper limb assessment (RULA) method with inertial measurement units fixed on the worker’s
body [Vignais et al., 2017]. Although the combination of the RULA method with inertial measure-
ment units has already been applied to surgeons [Carbonaro et al., 2021] [Maurer-Grubinger et al., 2021],
it would be relevant to use it for other hospital staff.
In the next chapter, we will review the existing literature to define our theoretical framework.
1

9. Then, we will set our goals and assumptions. Methods and materials for our study will be detailed in
a next chapter. It will be followed by the results of our study. Then, these results will be discussed.
Finally, we will conclude the study.
2

10. Chapter 2
Theoretical framework
Before setting our goals and assumptions, this chapter will review the existing literature about
this topic in three parts. The first one will deal with musculoskeletal disorders by defining them and
their causes before focusing on those affecting hospital staff. The second one will explore different
physical ergonomic assessment methods through their three families. The last part will focus on
systems combining observational methods with direct measurement tools in the general case and then
applied to hospital staff.
2.1 Musculoskeletal disorders among hospital staff
2.1.1 Definition and causes
Musculoskeletal disorders (MSDs) mean all problems such as injuries, diseases or disorders af-
fecting musculoskeletal system (mainly muscles, joints and tendons) then affecting human motion.
In 2012, they represented 87% of occupational diseases in France [Garoche, 2016]. In this case, one
talks about work-related MSDs.
Although they can occur on any body parts, work-related MSDs are more likely to affect upper
body, given the fact that most of occupational tasks are performed with upper limbs. Figure 2.1 shows
that main body parts affected by MSDs are shoulders, wrists, back, elbow and neck.
Risk factors causing work-related MSDs can be split into 3 main groups [Aptel et al., 2011]. The
first one is individual factors. As its name suggests, it gathers features of each individuals such
as age or sex but also inter and intra-individual variability. Muscular and psycho-sensory-motor
capacities are indeed never the same between two people and even between two limbs of a same
person [Aptel et al., 2011].
The second risk factors main group is environmental factors. They are the major causes of work-
3

11. Figure 2.1: Most common MSDs. Circle size is proportional to the MSD average unit cost multiplied
by its number of occurences.
Found in [Maurice, 2015]
related MSDs due to their connection with human motion solicitations and they are of two types.
First come biomechanical factors such as gestures repetitiveness, overexertion, static postures and
extreme joint positions. They never act in isolation but are always combined, which implies a certain
complexity. Psychosocial factors may be added to biomechanical factors, such as stress or mental
load. A stressed worker will be indeed more likely to perform gestures that are too fast, too intense,
too long and he/she will pay less attention to his/her posture. Plus, stress can weaken immune defense
and repair systems to heal from MSDs and amplify pain perception [Aptel et al., 2011].
The last risk factors main group is organizational factors. The latter are related to work organiza-
tion. Due to the ambiguous definition of this group, it is not unanimously accepted in the scientific
community. However, work organization has an influence on both biomechanical and psychosocial
factors. For example, inadequate work equipment may lead to bad postures or a too high rate of work
may imply repetitiveness. Moreover, these two cases may be synonymous with stressful situations.
If we cannot act on individual factors, we can act on biomechanical and psychosocial factors and
work organization to prevent work-related MSDs. However, these factors are never the same in every
occupational field and neither is their weight. Consequently, they have to be identified first. Hence
the need to perform physical ergonomic assessments, as we will see later.
2.1.2 Musculoskeletal disorders detected among hospital staff
Hospital field is obviously not exempted from work-related MSDs phenomenon. Qualitative and
quantitative studies have indeed reported upper body pains and injuries among different health pro-
4

12. fessions and tried to identify different risk factors.
Nurses especially suffer from lower back disorders [Carneiro et al., 2019] [Boughattas et al., 2017]
[Al-samawi et al., 2015]. This can be explained as well by individual factors such as sex, age, body
mass index, number of pregnancies, arthritis, physical condition [Boughattas et al., 2017] as by envi-
ronmental and organisational factors. Within the latter are found tasks such as patients handling and
equipment moving. In fact, they can be seen as biomechanical constraints that lead nurses to adopt
awkward postures and to apply excessive forces, in addition to their repetitiveness [Carneiro et al., 2019].
Finally, working load, poor working environment and layout of the materials have been identified as
other risk factors too [Boughattas et al., 2017] [Al-samawi et al., 2015].
Nursing assistants are exposed to more work-related MSDs risk factors than other nursing person-
nel [Ching et al., 2018]. In fact, their unlicensed status makes them more solicited for physical tasks
to take care of the daily needs of patients and they are less trained in ergonomic gestures. These tasks
include positioning patients to ensure their comfort, maintaining their personal hygiene but above all
transferring and lifting them. Patients are in fact transferred for regular necessities including break-
fast, bathing and lunch. Besides patient-related tasks, nursing assistants are sometimes also required
for other types of work such as cleaning and tidying rooms, delivering meals, taking care of clothes...
All these biomechanical demands act in a multifactorial way with psychosocial and organisational
factors. The former are, on one hand, psychological stress caused by pressure coming from their
work itself but also from patients, on another hand, psychological distress resulting from facing pa-
tients’ suffering. As for organisational factors, they include limited spaces, patients dependency, lack
of prework training, insufficiently maintained equipment and heavy workloads that can be increased
by staff shortages or new - then less experienced - workers. Hence, nursing assistants suffer from
work-related MSDs that affect various body parts and not only lower back.
Work-related MSDs can also take place in the operating room. A study has indeed highlighted
that a significant amount of surgeons indeed complain of pain too, especially on back, neck and hands
[Soueid et al., 2010]. Posture is one of the most cited risk factors for these workers. The latter is due
to an inappropriate operating surface height that can lead elbow joints and back to awkward positions.
Another important factor is operating instruments. In fact, they are designed above all for their func-
tionality and ergonomics and ease of use are often neglected. Surgeons have therefore to adapt their
handling, even if it means adopting uncomfortable hand positions. Plus, specific challenged are posed
by laparoscopic surgery instruments: the fact that they require static positions while operating and in-
crease risks of stiffness, especially for neck and trunk. To these factors we can add repetitiveness
[Carbonaro et al., 2021], operation duration, weekly time spent on operations but also individual fac-
tors such as professional situation (type of hospital, speciality, experience) and work-family conflict
5

13. [Dianat et al., 2018].
Consequences of MSDs among hospital staff have to be taken into account too. [Al-samawi et al., 2015]
point out that majority of the participating nurses complain of sleeping disturbances caused by their
pain and another majority reports that they have to restrict activity and movements. Furthermore,
most of suffering nurses declare to treat their symptoms either with non pharmacologic symptoms or
by combining them with analgesics. On the other hand, majority of suffering surgeons participating
to [Soueid et al., 2010] never take any measures to relieve pain. As for nursing assistants, the most
common impact of their MSDs is fatigue. In fact, they have little rest time and they face sleeping
disturbances due to their pain too. On another note, some of them use analgesics whereas seeking
medical attention and taking days off appear to be last resort solutions. The latter is generally avoided
because nursing assistants do not want to put their colleagues in difficulty through their absence
[Ching et al., 2018].
Finally, as mentioned in section 2.1.1, MSDs among hospital staff can be prevented by acting
on environmental and organisational factors. For example existing materials can be adapted through
ergonomic designs [Soueid et al., 2010]. Plus, new materials are designed to facilitate some man-
ual tasks [Collins et al., 2006]. Preventing MSDs can also be human-centered by proposing to hos-
pital staff ergonomic education programs when movement can be improved [Alghadir et al., 2021]
[Callihan et al., 2020]).
2.2 Physical ergonomic assessment methods to prevent muscu-
loskeletal disorders
2.2.1 Definition
Physical ergonomic assessment means methods and tools used to identify work-related MSDs
workers are exposed to. This is the first step in the path of risk prevention and reduction. In fact,
ergonomic studies are generally followed by recommendations to alter environmental and organisa-
tional factors, as mentioned in sections 2.1.1 and 2.1.2..
Various physical ergonomic assessment methods exist and they can be summarized in three fami-
lies: self-reports, observational methods and direct measurements [David, 2005] [Li and Buckle, 1999].
Each of them presents its advantages and its drawbacks.
6

14. 2.2.2 Self-reports
As their name suggests, self-reports are carried out by worker themselves. Different kinds of
data can be reported such as exposure to work-related risk factors but also demographic informa-
tion, experienced symptoms and level of exertion [David, 2005]. As for forms, they are various as
well. Self-reports can indeed be presented as body map, rating scales, questionnaires or checklists
[Li and Buckle, 1999].
The main advantage of these methods is their ease of use. They can therefore be applied to several
working cases at a low cost. However, collection of workers’ subjective data make self-reports more
subject to unreliability and imprecision. Plus, answers will not always be the same depending on
how each worker understands and interprets questions. Nevertheless, collection of subjective data is
useful to identify occupational groups showing relatively higher risk [David, 2005]. Here are a few
examples of self-report-based physical ergonomic assessment methods.
[Viikari-Juntura et al., 1996] study has developed a method composed by a self-administered ques-
tionnaire and a logbook. The questionnaire includes 150 ordinal-scale items of which ten are about
frequency and/or duration of weight lifting and adopted postures while the others are about muscu-
loskeletal symptoms. As for the logbook, it is composed of the following ordinal-scale items: fre-
quency of different weight lifting, duration of sitting, walking, standing, kneeling, squatting, driving
a motor vehicle, trunk and neck forward-bending, hands above shoulder level raising and manual ac-
tivities. The difference between these two documents is that the questionnaire is addressed to workers
once while the latter have to fill the logbook after each workhour during three workdays. The study
has shown that the questionnaire is useful to classify groups of tasks with respect to work-related risk
factors but with a low accuracy. On the other hand, the logbook appears to provide more valid in-
formation to study relationship between factors exposure and their effects. However, regularly filling
this document is not a straightforward task [Viikari-Juntura et al., 1996].
[Pope et al., 1998] study has developed a self-administered questionnaire to estimates features of
work physical demand for one workhour. It includes eight items on manual materials-handling, four
on postures and two on repetitive movements of the upper limbs. For each of these three topics,
the subjects have to estimate frequency on a categorical scale and duration. Plus, information on
weight is asked through visual analogue scales for manual materials-handling. The study has found
a satisfactory accuracy from the data provided by this questionnaire. This accuracy may decrease for
occupations requiring more various tasks though [Pope et al., 1998].
Another scale questionnaire has been developed by [Spielholz et al., 1999]. Based on analogical
and categorical scales, the questions concern physical stress exposure to the upper extremities, pro-
7

15. portion of each activity per day and frequency of different upper limb movements. Specificity of the
questionnaire is that it compares risk factors at primary work, secondary work and home activities,
which is interesting for temporary workers and those who change jobs frequently. Moreover, scales
are meant to be easily filled without disrupting work [Spielholz et al., 1999].
Videooch Datorbaserad Arbetsanalysis (Video and computer-based work analysis; VIDAR) is a
method that has the specificity of exploiting video recordings and computer-based techniques, as its
name suggests. This method works as follows: the worker carries out a work that can last for minutes
or hours while he/she is video filmed; the video recording is displayed to him/her on a computer
after the work and he/she uses a capture button everytime he/she detects a situation inducing pain or
discomfort according to him/her; when this button is clicked, a body map appears so that the worker
can click on one to three mainly affected body part; it is followed by a subjective rating scale on
which the worker rates his/her pain; he/she can add a comment if he/she wants; all this information is
stored in the computer memory; the video resumes until the next problematic situation appears to the
worker and the procedure starts again. According to [Kadefors and Forsman, 2000] study, VIDAR
is a relevant method for ergonomic assessments of complex work. Plus, possibility for the worker
to view the video recording right after his/her work so that he/she can remember easily problematic
situations.
In a nutshell, self-reports are useful to assess physical ergonomics among a large amount of sub-
jects, especially since the latter is necessary to get a representative overview of the surveyed groups.
Low levels of reliability and validity due to subjective data makes that they must however be used in
a complementary way to expert-based methods [David, 2005].
2.2.3 Observational methods
Observational methods are split into simpler and advanced methods.
Simpler observational methods A simpler or pen and paper-based observational method is carried
out by an observer who notes down factors he/she sees from a worker in activity on a worksheet.
Then the latter helps to assess risk factors exposure. According to the method, the factors noted
down by the observer can be posture, applied load/force, movement frequency, duration, recovery,
vibration but also more specific elements such as mechanical compression, glove use, environmental
conditions, equipment, load coupling, team work, visual demands, psychosocial or individual factors
[David, 2005].
Simpler observational methods have the main advantage of being inexpensive since they are
8

16. pen and paper-based. Plus, they can be carried out without disrupting workers activities. Nev-
ertheless, their reliability can be questioned by observations intermittency, implying a low preci-
sion. This is indeed problematic especially for dynamic tasks. Moreover, most of these methods
are unclear about an optimum number of observations depending on risk factors exposure variabil-
ity [Li and Buckle, 1999]. Here are a few examples of simpler observational methods for physical
ergonomic assessments.
One of the oldest simpler observational methods has been developed by [Priel, 1974]. This method
is based on a worksheet called Posturegram on which the observer sketches a worker posture. Then
this posture can be analyzed according to the different upper and lower limb positions that are ref-
erenced in the Posturegram. Although this method is intuitive and can be adapted to digital data
processing, sketching and analyzing is performed in several minutes, which is too long for dynamic
activities [Li and Buckle, 1999].
Ovako Working Posture Analysing System (OWAS) is a method developed by [Karhu et al., 1977].
To describe a worker posture, the observer has to select a position for each segment among those
showed by the worksheet: four for the back, three for the upper limbs, seven for the lower ones.
Each item corresponds to a code number so that the posture can be identified with a three-digit code.
Then the posture is evaluated according to its code corresponding to different segment positions. This
method has the advantage of requiring only a few seconds but the number of position items is too
limited to provide accurate posture analysis [Li and Buckle, 1999].
Hand-Arm-Movement Analysis (HAMA) has been developed by [Christmansson, 1994]. This
method focuses on upper limb movements through five biomechanical risk factors: basic motion,
grasp, upper limb position, external load and perceived exertion. For each of them, items are proposed
according to the type of motion in order to assess work-related stress [Christmansson, 1994].
Plan for Identifiering av Belstnings faktorer (Method for the identification of musculoskeletal
stress factors which may have injurious effects; PLIBEL) is a checklist developed to identify er-
gonomic hazard on different body regions. This checklist is composed of closed questions about work
posture awkwardness, work movement-induced fatigue, poorness of tool or workplace design and en-
vironmentally or organizationally induced stress. Each of these questions is addressed for at least one
of the five body regions: neck, shoulders and upper back; elbows, forearms and hands; feet; knees
and hips; lower back. PLIBEL is a useful screening tool to identify risk factors for musculoskeletal
injuries on specific body regions but it is subject to a low inter-observer reliability [Kemmlert, 1995].
Quick Exposure Check for work-related musculoskeletal (QEC) has been developed by [Li and Buckle, 1998].
It assesses upper body parts performing a certain task with respect to their postures and repetitive
movements but also task duration, maximum lifted weight, hand exertion, vibration, visual demand
9

17. and subjective responses to the work. Exposure levels are then determined with respect to these fac-
tors and their interactions. QEC appears to provide a good sensitivity in addition to ’fait to good’ inter
and intra-observer reliabilities [Li and Buckle, 1999].
Rapid Upper Limb Assessment (RULA) is an upper body assessment developed by [McAtamney and Corlett, 1
It consists in computing local and global risk scores with respect to upper body parts position but also
lifted weight and muscle exertion. This method will be detailed further and figure 2.2 shows its
worksheet. Moreover, it is the basis for another method, Rapid Entire Body Assessment (REBA),
developed by [Mcatamney and Hignett, 1995] by taking into account lower body.
10

18. Figure
2.2:
RULA
worksheet
Found
in
[Vignais
et
al.,
2013]
11

19. Advanced observational methods An advanced or video-based observational method does not
always require a human observer as for simpler methods. In fact, the worker is video filmed and a
computer analyzes postural variation through the recording [Li and Buckle, 1999]. Analysis may be
based on two or three-dimensional biomechanical models and anthropometric data to identify human
body postures [David, 2005].
The absence or the limited role of an observer allows to avoid observer bias. Plus, computer al-
lows simultaneous analyses on several joint segments. However, these methods require highly-trained
technicians to ensure effective operations. Another drawback is that camera position depends on op-
erator movements and analyzed video recordings can be subjects to occlusion [Li and Buckle, 1999]
[David, 2005]. Here are a few examples of advanced observational methods for physical ergonomic
assessments.
Hands Relative to the Body (HARBO) has been developed by [Wiktorin et al., 1995]. This method
that allows continuous analysis in real-time for up to several hours works as follows. A human
observer has to identify the work posture performed by the worker and more precisely placement of
his/her hands among five items: standing or walking with one or two hand(s) above shoulder level;
standing or walking with two hands between shoulder and knuckle levels; standing or walking with
one hand below knuckle level; standing or walking with two hands below knuckle level; sitting. Each
times an item is selected by the observer, the computer registers the duration of the posture. Although
this method is cheap and easy to learn and to use, it only registers a limited number of postures to
keep a good inter-observers reliability [Wiktorin et al., 1995].
Portable Ergonomic Observation (PEO) is a real-time method developed by [Fransson-Hall et al., 1995].
As in HARBO, an observer continuously registers posture and activities of a worker by selecting items
on a computer. These items are however more various than in the former method, with choices for dif-
ferent body regions but also lifted weight and manual handling. For each posture or activity, duration
and frequency are computed by the software [Fransson-Hall et al., 1995].
To summarize, simpler observational methods are more appropriate for static jobs where the work-
ers hold postures for a long time or perform simple movements that are repeated so that the observer
can easily carry out his/her analysis and loose as little information as possible whereas advanced
methods are more suitable for dynamic jobs [Li and Buckle, 1999]. On the other hand, it is prefer-
able to use advanced methods for simulations rather than for practical assessments in the workplace
[David, 2005].
12

20. 2.2.4 Direct measurements
Direct measurements involve tools that provide quantitative data about posture, postural strain or
muscle fatigue. They can be either manual or electrical devices. The former are often cheap and
easy to use and they provide relevant information about body posture under static situations. As for
dynamic situations, electrical devices are preferable [Li and Buckle, 1999].
An example of manual device is flexicurve [Burton, 1986]. It consists in a flexible curve that
assesses lumbar sagittal mobility by bending in one plane to screen lower back disorders. The method
requires first to locate three spinal landmarks (S2, L4 and T12) on the subject so that the flexicurve can
be fixed on them while the subject performs a maximal lumbar flexion. Then the flexicurve is removed
and the shape it has adopted is reproduced on a paper sheet. The process is repeated with the subject
performing a maximal lumbar extension. Finally, for both curves, tangents are drawn on the three
landmarks and angles between them are measured to characterize lumbar sagittal mobility and then
to deduce lower back disorders from which the subject may suffer. Flexicurve is therefore a relevant
tool for lumbar region but is subject to limited intra and inter-observer reliabilities [Burton, 1986].
Accelerometer-based inclinometry can be used for posture analysis, as studied by [Hansson et al., 2001].
This consists of using triaxial accelerometers to deduce joint angles from angular accelerations.
[Hansson et al., 2001] have borne out the validity and precision of the system under static and quasi-
static conditions but the latter has not been tested for measuring human movements. However,
[Bernmark and Wiktorin, 2002] have evaluated an accelerometer-based inclinometry system for arm
movements. This method appears to provide a good precision for movements at normal to high veloc-
ities. Plus, the system is easy to use and to wear. However, single movements ate less easy to detail
at very high velocities.
Lumbar Motion Monitor (LMM) is an exoskeleton of the spine that analyses trunk movements in
three-dimensional space to prevent lower back disorders. It is attached to the pelvis and the thorax
of the subject and it is composed of T sections in the lumbar spines. These T sections follow the
movements of the subject’s lower back and they are connected to potentiometers that change voltages
with respect to these movements. Voltage signals are then converted into angular positions to be
analyzed by a computer. This is a useful technique for dynamic situations that shows a good accuracy
and an ease of signal processing [Marras et al., 1992]. Nevertheless, this dynamic aspect is limited
by the short maximum duration of continuous data collection (approximately 30 s). Moreover, LMM
does not take into account hip movements [Li and Buckle, 1999].
Electromyography (EMG) is a technique that collects electrical signals generated by muscle ten-
sion. It can be used to evaluate relative muscle activity but also local muscle fatigue [David, 2005]
13

21. [Li and Buckle, 1999]. However, evaluating the former requires to interpret EMG amplitudes based
on posture and electrode placements but also individual factors. As for local muscle fatigue, it can
be assessed by interpreting spectral features evolution such as amplitude and frequency. In fact, the
former increases and the latter decreases when fatigue occurs [Li and Buckle, 1999].
In a nutshell, direct measurement methods are useful to continuously provide different types of
highly accurate data, especially for electrical devices under dynamic situations. However, the latter
tools are not always cheap and they often require costs of maintenance. Plus, direct measurement
methods may cause discomfort for the worker and his/her work could be disrupted [David, 2005].
As we will see on the next section, physical ergonomic assessment methods combining observational
methods with direct measurements have been developed [Li and Buckle, 1999].
2.3 Combining observational methods and direct measurements
for physical ergonomic assessment
2.3.1 General case
Combining different methods may be a relevant idea to take advantage of the benefits of the
methods while compensating for their limitations. For example, [Wells et al., 1994] have developed a
mixed method to assess risk factors of work-related MSDs affecting upper body. To do so, they have
combined quantitative data about musculoskeletal stresses with video recordings to estimate postures
but also EMG to estimate muscle activity and goniometers to measure wrist flexion/extension and
abduction/adduction. Hence, data estimation by the two latter tools is complementary to video ob-
servations that are limited and superposing all this data has appeared to be useful for epidemiological
studies [Wells et al., 1994].
[Plantard et al., 2017] have developed a system combining the RULA method with a markerless
motion capture tool (Microsoft Kinect). The latter measures joint angles of the subject through its
three-dimensional camera and these angles are used as input arguments for the RULA method. This
system has been evaluated in a laboratory condition to compare it with a marker-based motion capture
tool and then in a workplace condition to compare it with a pen-based assessment performed by two
experts. In the first condition, few differences occurred between both system, while in the second one,
the Kinect-based system provided more accurate data. This method is nevertheless limited by Kinect
light sensitivity, its occlusions and the fact it cannot take into account information such as frequency
of movements or force exerted [Plantard et al., 2017].
Another direct measurement tool that can be combined with observational methods for physical
14

22. ergonomic assessment and that appears to be promising is inertial measurement units (IMU). These
sensors that provide kinematic data have indeed the advantage of being cheaper than other tools
[Vignais et al., 2017]. Therefore, studies have presented different ways to combine IMU systems with
observational methods and they have applied them to different occupations. In accordance with them,
data collection and processing can be either in real-time, possibly with a feedback for the subject, or
afterwards.
[Vignais et al., 2013] have developed a system combining IMUs on the upper body and goniome-
ters on wrists with the RULA method. In their study, data is collected and processed in real-time so
that the worker can get a visual feedback of the RULA scores via a see-through head mounted display
(STHMD) and auditory warnings when he/she adopt awkward postures, as shown in figure 2.3. For
experimental assessment, half of subjects were wearing the system, and the other half did not get
any feedback. Results have showed the group with RULA feedback had lower scores, adopted less
awkward postures, than the group without RULA feedback [Vignais et al., 2013].
Figure 2.3: Subject wearing IMUs, goniometers and STHMD getting a RULA feedback
Found in [Vignais et al., 2013]
A similar system developed by [Battini et al., 2014] combines IMUs on the entire body with dif-
ferent methods such as RULA, OCRA, OWAS, lifting index and other ergonomic features such as
hands positions and hip movements. The most suitable method can be selected by the user with the
help of a software module. Plus, the user can choose whether the data is processed in real-time or
15

23. afterwards. If the first option is selected, ergonomic results are provided as a visual feedback through
a portable screen or a personal computer. In the study, the system has been applied to two warehouses
and it has allowed to identify their risk factors of work-related MSDs [Battini et al., 2014].
Another real-time assessment system has been developed by [Huang et al., 2020]. Based on IMUs
on the entire body too, it computes RULA and REBA scores and it performs a two-dimensional
(2D) static biomechanical analysis to compute lower back compression force. Then, a graphical
user interface (GUI) of the automated RULA and REBA displays these global and local scores with
other information such as kinematic data but also average scores, duration and score distributions.
In addition, another GUI of the automated 2D static biomechanical analysis tool displays lower back
compression force with descriptive statistics, and the sagittal view of the worker’s subject. The system
design is summarized in figure 2.4. The study has proceeded with validation of the developed system
by making subjects perform tasks with it. Hence, RULA and REBA scores have appeared to be
reliable. However, the validation experiment was conducted in a lab environment instead of a field
one [Huang et al., 2020].
Figure 2.4: Conceptual design of a system combining RULA/REBA and IMUs
Found in [Huang et al., 2020]
An offline system developed by [Vignais et al., 2017] combines the RULA method with IMUs on
the upper body, goniometers on wrists but also video recordings. The addition of the latter permits
indeed to identify which subtasks are performed in an occupational environment and their related
scores. In the study, the system has been tested on laboratory workers. Although the data is not
processed in real-time, post-processing appears to be reliable to identify risk factors. In fact, subtask
analysis thanks to video recordings permits to detect awkward subtasks. Thus, identification of risk
factors may be carried out more easily [Vignais et al., 2017].
2.3.2 Among hospital staff
All of the above-mentioned combined methods have been applied either in a lab environment
or in field. Yet, none of the in-field studies involved hospital professions. Moreover, among the
studies mentioned in section 2.1.2 and involving hospital staff, only [Callihan et al., 2020] uses a
16

24. system combining observational methods with self-reports whereas others use only self reports and/or
observational methods. In fact, this study consists in using IMUs on the entire body to measure lever
arm distance among nurses practicing patient manual lifting, as showed in figure 2.5. Data was
collected before and after a training intervention to learn how to carry out proper movements and
significant changes have been shown in between, hence reducing risk factors of lower back pain.
However, this study is limited to one task and manual lifting is performed on manikins instead of real
patients, which would have been more realistic [Callihan et al., 2020].
Figure 2.5: Nurse wearing IMUs during a patient lifting simulation
Found in [Callihan et al., 2020]
Another system combining the RULA method with IMUs and video recordings has been applied
to a surgeon performing a laparoscopic operation. Video recordings are used to identify surgical steps
during the operation and focus on them when computing RULA scores thanks to kinematic data from
IMUs placed on the lower and upper back and the head. These scores have appeared to be high,
confirming the risky postures adopted by the surgeon. This study is nevertheless limited by the fact
that it has been applied to only one subject and that the overall RULA scores could not be estimated.
In fact, only three IMUs are used and they permit only to provide information about head, neck
and trunk. On the other hand, more IMUs could have disturbed the surgeon while he was operating
[Carbonaro et al., 2021].
A similar system has been applied to oral and maxillofacial surgeons but with IMUs placed on the
entire body this time and RULA scores have been achieved, with significant differences between the
two analyzed working conditions. However, the operation is performed on a dummy head instead of
real patients [Maurer-Grubinger et al., 2021].
17

25. Chapter 3
Goals and Assumptions
According to the studies mentioned in the previous chapter, combining the RULA method with
IMUs for continuous physical ergonomic assessment on the upper body appear to be a relevant system.
In fact, IMUs permit to apply a simpler observational method to dynamic situations by providing
accurate kinematic data, in addition to being less expensive than most other direct measurement tools.
Furthermore, adding video recordings may be considered relevant too. This may indeed help not only
to identify when the work is performed so that post-processing can focus on these moments but also
to identify subtasks performed by the workers and then to carry out modification of the occupational
environment according to them.
It is known that hospital staff are regularly victims of work-related MSDs, especially on the upper
body. A few studies have conducted physical ergonomic assessments by combining the RULA method
(or other observational methods) with IMUs. However, they are limited either by the low number of
performed tasks or the low number of subjects or the low number of analyzed upper body parts or
their simulated appearance. Plus, these studies involved nurses or surgeons and not other hospital
staff such as those cleaning and tidying up operating rooms after surgical operations. Yet, these tasks
involve several biomechanical risk factors.
Consequently, the main goal of this study is to conduct in-field physical ergonomic assessments
for hospital staff specialized in disinfection tasks and patient displacements according to subtasks
they perform. This would indeed be a first step to identify risk factors, especially awkward subtasks,
of upper body work-related MSDs and then to prevent the latter. To do so, this thesis aims to develop
a system combining the RULA method with IMUs and video recordings.
IMUs have to be placed on the worker’s upper body to collect kinematic data and a video camera
has to record his/her motion while he/she does his/her jobs. After its collection, this data has to be
processed afterwards to compute ergonomic scores following the RULA method. Then, these scores
18

26. have to be analyzed according to their corresponding subtasks identified through video recordings.
Our first assumption is that average RULA scores would be worth 5 or more, thus associated with
awkward postures. In fact, this might be the cause of MSDs affecting upper body among hospital
staff.
Our second assumption is that local scores whose duration appears to be mostly spent at a risky
level would be related to upper body parts on which hospital staff complain of suffering from MSDs.
Thus, the latter might be explained by the risky positions adopted by their corresponding upper body
segments. To answer this assumption, a self-administered questionnaire for physical ergonomic as-
sessment, the Nordic musculoskeletal questionnaire, has to be addressed to the subjects so that sub-
jective feelings can be compared with objective measurements.
Our last assumption is that subtasks whose RULA scores systematically show significant superi-
ority over other ones would be the same than those leading to MSDs according to literature dealing
with hospital staff.
In the next chapter, we will detail the study that has been conducted to achieve our goals and
answer our assumptions. It will be followed by the report of all results obtained by the study. Then, we
will discuss these results in relation to our goals and assumptions but also limitations and perspectives
of the study. The last chapter will be the conclusion of the study.
19

27. Chapter 4
Materials and Methods
4.1 Participants
Nine workers participated to this experiment (M = 8; F = 1), including one whom we cannot
use all the data, as we will explain later. They had an average age of 38.67±13.64 years, an aver-
age experience of 10.80±11.66 years, an average height of 176.38±4.27 cm and an average weight
85.33±14.45 kg. They were recruited through flyers (Appendix 1) distributed in the hospital where
we performed the experiment but also by sending them an e-mail with the help of the hospital.
Participants had to be hospital staff performing disinfection tasks in operating rooms and patient
displacements after surgical operations with a minimum of one year experience. In fact, as mentioned
in section 2.1.2, they belong to a professional category that frequently suffers from work-related
MSDs. Yet, most studies involved nurses or surgeons. They had to be at least 18 years old and they
had to have no injuries affecting the movement of the upper limbs for less than 6 months.
Each participant was volunteer and he/she was allowed to stop the experiment at any time without
any consequence. Before participating to the study, we had first informed him/her about what would
happen during the experiment. Plus, we had made him/her sign an informed consent form (Appendix
2) and an image right form (Appendix 3).
As an experiment involving human subjects and not intended at the advancement of biological
or medical knowledge, our experimental protocol and our data acquisition and conservation meth-
ods have been reviewed by Research Ethics Committee (CER) for approval (Université Paris-Saclay,
2021-293; Appendix 4).
20

28. 4.2 Materials
4.2.1 Data acquisition materials: XSens inertial measurement units system
and video recordings
IMUs have been used to record workers’ movements. In fact, as mentioned in section 2.3.1, an
IMU is a sensor used to measure kinematic data of a moving part such as linear and angular positions,
velocities and accelerations. We have therefore used the wireless motion tracker system MTw Awinda
by XSens to get kinematic data about upper body segments. The system was composed of 11 motion
trackers: one on each shoulder, on each upper arm, on each forearm and on each hand, one on the
head, one on the sternum and one on the sacrum.
MTw Awinda has a first advantage of being wireless. Such a feature is necessary in our experiment
environment. We could indeed meet a huge amount of obstacles in operating rooms such as medical
devices or other workers and cables would quickly become a burden. Plus, the system enables very
high data transmission rates (up to 60 Hz when 11 motion trackers are used), retention of data packets
and accurate time synchronization between trackers. It provides therefore a high performance despite
the fact that it is not a traditional cabled system [Paulich et al., 2018].
Figure 4.1: XSens motion tracker and IMUs system
Left image found in the XSens website
Another important benefit is the software MVN Analyze provided by XSens to visualize move-
ments performed with the MTw Awinda system and to read their kinematic data in real-time and/or
afterwards. In fact the MVN Analyze system can interact with other softwares such as Matlab, both
in real-time and afterwards. Thus it was useful for us to import our kinematic data into our Matlab
21

29. program to compute ergonomic scores.
Figure 4.2: Subject moving with the IMUs system visualized on MVN Analyze and on video record-
ings
Finally, in addition to IMUs, we have used a camera to record our subjects movements as videos.
This has helped us to identify and to segment in time different subtasks performed by hospital staff.
4.2.2 Rapid upper limb assessment method for ergonomic analysis
Rapid upper limb assessment (RULA) is an observational method for ergonomic assessment.
Based on postures, forces and muscles actions, this tool was developed to evaluate workers exposed
to risk factors of upper-body musculoskeletal disorders.
The method, as summarized in figure 2.2, works as follows [McAtamney and Corlett, 1993].
Biomechanical data at different upper body segments, mainly joint angles but also a few positions,
are used to compute local ergonomic scores with respect to them. Then the latter are taken as in-
put arguments in look-up tables to compute three posture scores: one for each side (shoulder, elbow
and wrist) and one for the central part (neck, trunk and legs). Two other scores are added to each
of these intermediary scores: a muscle use score computed with respect to the posture held and the
repetitiveness of the action performed by the worker; a force/load score computed with respect to the
load lifted by the worker. Then, the modified intermediary scores for the left side and the central part
are taken as input arguments in a look-up table to finally compute the global left score. The latter
operation is repeated with the modified intermediary scores for the right side and the central part as
input argument to compute the global right score.
Whether it is local, intermediary or global, the higher a score is, the more likely it is that the worker
22

30. will suffer from musculoskeletal disorders. Global scores are interpreted as follows [McAtamney and Corlett, 1993
• 1 or 2 = acceptable posture;
• 3 or 4 = further investigation, change may be needed;
• 5 or 6 = further investigation, change soon;
• 7 = investigate and implement change.
We can see that RULA calls for algorithmic methods such as conditional statements and look-up
tables. Thus we could easily implement these methods in a Matlab program that will take kinematic
data of the IMUs as input arguments, as mentioned in section 4.2.1.
4.2.3 Nordic musculoskeletal questionnaire
Nordic musculoskeletal questionnaire is a standardised questionnaire developed to analyze a worker’s
musculoskeletal disorders. It consists of binary and multiple choices questions about musculoskeletal
state that can be answered directly by the involved worker.
The questionnaire starts with general questions such as working conditions, physiological infor-
mation (age, sex, height, weight) and if the worker is right-handed or left-handed. Then it is followed
by a summary part where the worker indicates body zones at which he/she recently suffered from
troubles. Finally the questionnaire is concluded by specific parts with more precise questions about
zones at which the worker already suffered from troubles [Kuorinka et al., 1987] (Appendix 5).
Nordic musculoskeletal questionnaire is widely used in different project involving working phys-
ical conditions due to its reliability and its trade-off between accuracy and accessibility of the ques-
tions. A Nordic musculoskeletal questionnaire was filled by every subject before starting the experi-
ment. Therefore we were able to compare the answers to local ergonomic scores.
4.3 Experimental procedure
The experiment took place in Pitié-Salpêtrière hospital in Paris, in the Gaston Cordier building
that houses operating rooms for different surgery departments.
An experimental procedure that follows a scientific accuracy was necessary to meet our goals. In
our case it was a quantitative field study involving a single group of subjects and detailed below.
First of all, we welcomed the subject in our temporary office. Then we made him/her sign the
informed consent form explaining everything that would happen next and an image right form so that
we could film him/her during the experiment.
23

31. Once the administrative step was achieved, we could now prepare the subject for the experiment.
While he/she firstly had to fill a Nordic musculoskeletal questionnaire, we took measurements of
his/her height and foot length (shoes included) for the MVN Analyze software. After that we made
him/her take off his/her work coat and cap so that we could put the IMUs system on him/her with
switched on motion trackers. Then we made him/her put on his/her work coat and cap back. If he/she
did not have a long-sleeved coat, we provided him/her with one so that arms motion trackers were
protected from water. This preparation was concluded by a calibration phase.
Figure 4.3: Subject wearing the IMUs system under working clothes
When a surgical operation was over, the subject had to come to the operating room to clean it and
tidy it up. Therefore, we could start the experiment. Before entering the operating room, the subject
put gloves on to protect hands motion trackers from water and we started video and IMUs recordings.
At the beginning of those, we asked the subject to clap his/her hands so that we would be able to
synchronize both of them for post-processing. Then we let the subject do his/her job normally while
we were recording his/her movements through the IMUs system and the video camera.
Once the job was done, we made the subject clap his/her hands a second time for the same reason
as the first one and we stopped recordings. Finally we came back to our temporary office where
we removed the IMUs system and we made him/her fill a feedback comfort questionnaire. The
experiment was henceforth over for the subject and we cleaned the motion trackers, straps, jacket,
headband and gloves with wipes and sanitizer before starting the next session.
Each session lasted between 40 and 60 minutes, depending on how long it took to clean and to
24

32. tidy up the operating room, to wash the tools and to transfer the patient. All along the experiment,
sanitary rules were scrupulously respected, especially due to the covid-19 context.
4.4 Data acquisition and processing
4.4.1 Ergonomic scores computing
As mentioned in section 4.2.2, ergonomic scores were computed through a Matlab program that
follows the RULA method structure based on conditional statements and look-up tables. To do so we
needed two kinds of input data.
The first one was kinematic data such as joint angles and segment positions acquired through the
motion trackers of the IMUs system. We needed to import them from the MVN Analyze software to
Matlab. In fact motions of each subject were registered in an MVN file in which they are reproduced
as a three-dimensional (3D) animation. We first had to convert it into an MVNX file so that we could
extract the joint angles and segment positions we were interested in. This file was used as an input
argument for the Matlab program.
The second type of input data was the weight lifted by the subject. To determine it, we had to
analyze video recordings to see which objects were lifted and when and to find the corresponding
time frames on MVN Analyze. Then we created an array whose length is equal to the total number
of frames of the subject and we added the weight lifted at each corresponding index. This array was
the other input argument for the Matlab program.
The Matlab program was composed of different scripts. A main one was based on a file provided
by XSens basically to import MVNX files into Matlab. We modified it so that it could also take the
weight array as an input argument and call other scripts that compute RULA ergonomic scores. The
output data was a structure composed by arrays. Each array represented a type of score and it contains
its temporal evolution.
Nevertheless three parameters could not be measured either by IMUs or through video observa-
tion: shoulder raising statement, leg score and muscle use score. Based on our direct observations,
we set these parameters by default: shoulders were never raised; legs and feet were always supported
then the leg score was equal to 1; posture was never static for more than ten minutes and actions were
never repeated four times per minute or more then the muscle use score was null.
25

33. 4.4.2 Subtasks segmentation based on video processing
Based on our observations, we have identified 27 subtasks performed by subjects. To segment
them with respect to time for each subject, we exploited video and MVN files as follows.
When we detected a task completion in a video file, we noted down its start and end times. Then
we found the equivalent frames in the MVN file where this subtask started and ended, hence the
interest of making the subject clap at the beginning and the end of recordings. Finally we noted these
frames down in an Excel file of the subject with all the subtasks performed by him/her and their
frames.
Due to the high number of subtasks performed, we decided to gather some of them to finally get
17 subtasks, as we can see in table 4.1.
4.4.3 Features extraction for each subtask of each subject
After identifying and segmenting each subtask for each subject, we had to extract their features
so that we could analyze them. To do so, we had to create new arrays representing their scores on
Matlab. Those were actually created by concatenating parts of full score arrays whose first and last
indexes corresponded to the frames from the MVN files we noted down in the Excel file.
Thanks to these new arrays, we could compute features for each score of each subtask of each
subject. These features were mean scores and percentage of time spent at each result. Moreover, for
global scores, we computed percentage of time spent at each range defined in section 4.2.2 and for
local scores, we focused on percentage of time spent at a risky level based on the following predefined
thresholds [Vignais et al., 2017] [Vignais et al., 2013]:
• Shoulder and upper arm: 5
• Elbow and lower arm: 3
• Wrist and hand: 5
• Neck and head: 4
• Pelvis and trunk: 4
Finally we determined mean scores of each subtask over all subjects so that we could perform statis-
tical tests.
26

34. Group of subtasks Subtask
Waste disposal
Waste pickup
Garbage disposal
Handling of lighting
Lighting protections removal
Lighting switching off
Lighting cleaning
Radiography device handling
Handling around the patient
Patient unequipping
Patient equipping
Patient holding
Installing patient on stretcher
Cables and pipes handling
Pipe disconnection
Cables untangling
Patient transfer Patient transfer
Surfaces and tools cleaning
Equipments cleaning
Sink cleaning
Various objects moving
Various objects moving
Water disinfection
Floor cleaning Floor cleaning
Boxes lifting Boxes lifting
Water tanks handling Water tanks handling
Pressure washing Pressure washing
Operating table moving Operating table moving
Stretcher moving Stretcher moving
Operating table cleaning Operating table cleaning
Operating table disassembly Operating table disassembly
Sheets moving Sheets moving
Trolley moving Trolley moving
Table 4.1: Subtasks gathering
27

35. 4.4.4 Data analysis
The goal of our statistical tests was to determine whether some subtasks got significantly higher
scores and if so which ones. Due to the weak number of subjects with usable data (= 8), we performed
two non-parametric tests:
• A Friedman test to demonstrate a significance between substasks;
• A Wilcoxon test to find the subtasks that are significantly different from the others.
For both tests, independent variables were subtasks while dependent ones were subject’s average
RULA scores. They were performed on R.
28

36. Chapter 5
Results
5.1 Global RULA scores
On average, subjects performed their subtasks with a global RULA score of 4.21±1.15 for the
right side and 4.19±1.20 for the left one. This means that the average posture needs further investi-
gation and change may be needed [McAtamney and Corlett, 1993].
Furthermore the biggest part of time is spent on average at range 3-4 with a percentage of 63.54±31.59%
for the right side and 64.33±32.33% for the left one. It is followed by range 5-6 with a percentage
of 19.38±20.58% for the right side and 17.37±19.34% for the left one then the range 7 with a per-
centage of 13.98±24.52% for the right side and 14.97±25.54% for the left one. Finally the smallest
part of time is spent on average at range 1-2 with a percentage of 3.09±5.02% for the right side and
3.32±4.52% for the left one. These percentages are plotted in figure 5.1.
Figure 5.1: Mean percentage of time spent at each RULA range
Finally an example of global RULA score time evolution is plotted in figure 5.2, followed by a
zoom on a subtask (represented in the red rectangle in figure 5.2) in figure 5.3.
29

37. Figure 5.2: An example of global RULA score time evolution: Subject n°6’s left score
Figure 5.3: Subject n°6’s left score time evolution zoomed on a subtask: Various objects moving
30

38. 5.2 Local RULA scores
For each local RULA score, table 5.1 points its mean value and standard deviation. All of them
are under the risky threshold [Vignais et al., 2017] [Vignais et al., 2013].
Location Mean RULA score
Right upper arm 1.75±0.39
Left upper arm 1.68±0.34
Right lower arm 2.33±0.30
Left lower arm 2.37±0.28
Right wrist 4.50±0.37
Left wrist 4.41±0.40
Trunk 2.41±0.70
Neck 1.29±0.36
Table 5.1: Mean local RULA scores and standard deviations
Furthermore local scores with the biggest part of time spent at a risky level are the wrists ones
with a percentage of 50.52±19.56% for the right side and 46.95±17.80% for the left one. They are
followed by the lower arms with a percentage of 43.55±21.27% for the right side and 45.37±22.27%
for the left one then the trunk with a percentage of 11.50±14.40% and the neck with a percentage of
4.44±10.88%. Finally the upper arms scores are those with the smallest part of time spent at a risky
level with a percentage of 0.38±1.15% for the right side and 0.54±2.08% for the left one. These
percentages are plotted in figure 5.4.
Figure 5.4: Mean percentage of time spent at a risky level for each local RULA score
Finally an example of local RULA score time evolution is plotted in figure 5.5, followed by a
31

39. zoom on a subtask (represented in the red rectangle in figure 5.5) in figure 5.6.
Figure 5.5: An example of local RULA score time evolution: Subject n°6’s left upper arm score
5.3 Subtask analysis
For each local subtask, mean global and local RULA scores and standard deviations are given in
table 5.2. We can notice that those associated to the highest global score are ’Stretcher moving’ for
the right side (6.27±0.28) and ’Operating table moving’ for the left one (6.36±0.87). Right upper
arm reaches its highest score during ’Pressure washing’ (2.07±0.67) while left one reaches it during
’Handling of lighting’ (2.23±0.39). Lower arms riskiest subtasks are ’Floor cleaning’ and ’Operating
table cleaning’ for the right side (respectively 2.57±0.14 and 2.57±0.25) and ’Water tanks handling’
for the left one (2.68±0.22). The latter task is also the riskiest one for right wrist (4.79±0.48) whereas
it is ’Patient transfer’ for left one (4.84±0.46). Finally neck is most exposed during ’Operating table
moving’ (1.69±0.76) and trunk during ’Operating table cleaning’ (2.96±0.47).
32

40. Figure 5.6: Subject n°6’s left upper arm score time evolution zoomed on a subtask: Various objects
moving
33

41. Global
score
Upper
arm
score
Lower
arm
score
Wrist
score
Neck
score
Trunk
score
Right
Left
Right
Left
Right
Left
Right
Left
Waste
disposal
3.31±0.19
3.27±0.15
1.64±0.26
1.49±0.16
2.34±0.05
2.43±0.20
4.30±0.33
4.23±0.21
1.18±0.09
2.45±0.46
Handling
of
lighting
3.65±0.48
3.76±0.54
2.05±0.32
2.23±0.39
2.10±0.28
2.18±0.23
4.53±0.39
4.34±0.26
1.62±0.45
2.13±0.72
Handling
around
the
patient
3.53±0.38
3.47±0.38
1.75±0.37
1.63±0.16
2.33±0.38
2.30±0.26
4.49±0.35
4.47±0.42
1.30±0.28
2.12±068
Cables
and
pipes
handling
3.42±0.17
3.37±0.21
1.73±0.40
1.75±0.27
2.16±0.35
2.20±0.24
4.41±0.27
4.32±0.27
1.19±0.16
2.73±0.69
Patient
transfer
4.95±1.05
4.98±1.12
1.70±0.48
1.66±0.30
2.51±0.36
2.28±0.29
4.78±0.38
4.84±0.46
1.55±0.89
2.04±0.80
Surfaces
and
tools
cleaning
3.61±0.32
3.58±0.41
1.67±0.27
1.66±0.23
2.31±0.23
2.31±0.35
4.32±0.27
4.23±0.18
1.33±0.20
2.35±0.64
Various
objects
mov-
ing
3.85±0.50
3.81±0.57
1.63±0.24
1.51±0.15
2.31±0.21
2.32±0.26
4.29±0.21
4.18±0.41
1.31±0.19
2.36±0.79
Floor
cleaning
3.80±0.53
3.79±0.57
1.96±0.46
1.69±0.24
2.57±0.14
2.50±0.10
4.64±0.29
4.70±0.40
1.13±0.13
2.95±0.72
Boxes
lifting
5.35±1.45
5.33±1.59
1.70±0.53
1.71±0.44
2.36±0.18
2.41±0.15
4.56±0.32
4.48±0.35
1.30±0.27
2.32±0.76
Water
tanks
handling
3.35±0.55
3.62±0.51
1.61±0.30
1.93±0.57
2.27±0.36
2.68±0.22
4.79±0.48
4.69±0.22
1.31±0.34
2.63±0.73
Pressure
washing
3.88±0.44
3.68±0.50
2.07±0.67
1.66±0.20
1.94±0.53
2.24±0.38
4.65±0.64
4.40±0.30
1.38±0.42
2.38±0.83
Operating
table
mov-
ing
6.24±0.95
6.36±0.87
1.56±0.43
1.72±0.33
2.44±0.34
2.52±0.39
4.25±0.39
4.23±0.69
1.69±0.76
2.88±0.50
Stretcher
moving
6.27±0.28
6.30±0.33
1.63±0.27
1.54±0.36
2.33±0.20
2.51±0.28
4.57±0.48
4.59±0.46
1.10±0.10
1.89±0.58
Operating
table
cleaning
3.80±0.54
3.56±0.52
1.72±0.24
1.54±0.39
2.57±0.25
2.43±0.31
4.66±0.30
4.17±0.42
1.31±0.22
2.96±0.47
Operating
table
dis-
assembly
4.48±0.56
4.47±0.68
1.73±0.25
1.70±0.32
2.21±0.33
2.37±0.12
4.47±0.30
4.34±0.30
1.17±0.22
2.65±0.55
Sheets
moving
3.27±0.29
3.18±0.22
1.80±0.56
1.68±0.51
2.23±0.16
2.16±0.42
4.40±0.39
4.31±0.36
1.11±0.09
2.15±0.66
Trolley
moving
6.14±0.74
6.18±0.83
1.71±0.45
1.71±0.34
2.55±0.20
2.63±0.14
4.47±0.20
4.48±0.25
1.19±0.08
2.34±0.80
Table
5.2:
Mean
global
and
local
RULA
scores
and
standard
deviations
for
each
subtask
34

42. Furthermore mean percentages of time spent at each RULA range for global scores and at a risky
level for local ones are given for each subtask in table 5.3. The subtask that makes spend the most
of time at a RULA range of 7 is ’Operating table moving’ for both sides (right: 65.66±37.73%; left:
73.67±27.77%). This subtask also makes neck spend the most of time at a risky level (20.89±25.60%).
’Sheets moving’ and ’Handling of lighting’ induce the highest percentage of time at this level for up-
per arms, respectively for the right side (1.65±3.36%) and the left one (6.97±5.78%). Right lower
arm reaches its risky level for the largest proportion during ’Floor cleaning’ (63.27±10.44%) while
left one reaches it during ’Water tanks handling’ (72.85±15.60%). The latter subtask also makes right
wrist spend the most of time at this level (64.43±37.55%) whereas it is ’Patient transfer’ for left one
(69.46±23.19%). Finally ’Floor cleaning’ also induces the highest proportion at this level for trunk
(33.47±30.46%).
As mentioned in section 4.4.4, Friedman tests are performed on global scores to compare subtasks.
This kind of test requires a complete block design without repetitions, i.e. each subtask has to have
the same number of data. Yet not all subjects did all the subtasks since it depended on their work.
Thus missing data are replaced by the median of the subtask they belong to so that statistical features
are slightly altered [CNA, ]. The Friedman tests have provided the following results:
• For the right side: χ2
= 95.098, df = 16, p-value = 2.849×10−13
• For the left side: χ2
= 89.108, df = 16, p-value = 3.651×10−12
From these 2 very low p-values, we can reject the assumption that there is no significant difference
between subtasks on both sides. We therefore have to know which subtasks are significantly different
from the others.
As mentioned in section 4.4.4, Friedman tests are followed by Wilcoxon tests to compare each
subtask 2 by 2. This kind of test does not require a complete block design without repetitions. We
can then perform them with missing data. Table 5.4 summarizes number of times each subtask shows
a significant difference with other ones, i.e. when p-value is lower than the significance threshold
(= 0.05). Subtasks showing the most frequently significant differences are ’Operating table moving’,
’Stretcher moving’ and ’Trolley moving’. Other subtasks such as ’Waste disposal’, ’Patient transfer’,
’Operating table disassembly’ and ’Sheets moving’ present a high number of significant differences
too.
35

43. Percentage of time spent at each RULA range Percentage of time spent at a risky level for each local RULA score
Upper arm score Lower arm score Wrist score Neck
score
Trunk
score
Right Left Right Left Right Left Right Left
1-2 3-4 5-6 7 1-2 3-4 5-6 7
Waste disposal 4.93
±
5.01
86.16
±
5.70
8.77
±
5.27
0.14
±
0.16
5.55
±
3.92
86.49
±
6.61
7.82
±
5.14
0.14
±
0.14
0.32
±
0.59
0.35
±
0.72
42.20
±
3.11
48.46
±
16.04
41.53
±
12.67
39.59
±
9.50
2.96
±
3.02
9.39
±
6.58
Handling of light-
ing
7.57
±
7.15
73.81
±
13.44
15.55
±
14.16
3.06
±
4.84
5.39
±
4.15
72.56
±
14.42
15.74
±
11.91
6.31
±
6.14
1.33
±
1.63
6.97
±
5.78
29.56
±
11.82
32.10
±
15.83
53.48
±
16.97
45.25
±
13.10
16.96
±
16.36
6.64
±
11.61
Handling around
the patient
3.89
±
4.36
83.02
±
14.77
9.45
±
8.92
3.64
±
4.57
4.14
±
3.66
83.55
±
15.11
10.22
±
11.18
2.09
±
2.46
0.33
±
0.56
0 44.27
±
25.44
37.47
±
21.82
50.42
±
21.01
46.97
±
20.37
3.45
±
4.56
5.29
±
5.76
Cables and pipes
handling
3.40
±
4.55
86.77
±
10.74
9.83
±
7.05
0 1.50
±
1.84
91.54
±
4.78
6.56
±
4.75
0.41
±
0.64
0.69
±
1.47
0.07
±
0.13
33.38
±
22.50
36.77
±
18.12
47.09
±
14.82
40.61
±
9.61
0.83
±
1.14
13.60
±
8.64
Patient transfer 0.25
±
0.44
44.39
±
35.45
30.19
±
30.42
25.17
±
25.32
1.23
±
2.09
42.69
±
36.65
31.52
±
30.08
24.56
±
22.79
0.44
±
1.17
0 55.33
±
31.22
33.95
±
24.28
64.42
±
25.64
69.46
±
23.19
16.07
±
27.55
2.08
±
3.56
Surfaces and
tools cleaning
3.46
±
4.46
81.34
±
10.03
12.35
±
8.29
2.85
±
3.07
4.52
±
5.37
81.08
±
10.17
11.14
±
7.81
3.26
±
4.66
0.05
±
0.14
0.47
±
0.98
41.11
±
18.69
44.38
±
26.42
39.63
±
9.85
39.02
±
11.01
2.05
±
2.39
8.33
±
6.88
Various objects
moving
3.84
±
5.75
73.62
±
12.63
14.20
±
5.41
8.34
±
9.49
4.11
±
4.90
73.47
±
14.60
14.32
±
7.01
8.11
±
9.83
0.47
±
0.98
0.09
±
0.18
43.01
±
14.00
44.25
±
15.61
39.72
±
9.50
37.89
±
14.70
3.58
±
5.50
9.46
±
9.48
Floor cleaning 1.14
±
2.05
76.10
±
20.08
22.60
±
21.10
0.16
±
0.17
2.48
±
3.78
74.36
±
17.33
22.96
±
19.74
0.19
±
0.27
0.26
±
0.66
0.14
±
0.20
63.27
±
10.44
53.65
±
8.37
57.70
±
12.22
59.83
±
21.94
0.28
±
0.37
33.47
±
30.46
Boxes lifting 0.22
±
0.55
37.32
±
45.34
27.77
±
33.71
34.69
±
37.37
0.28
±
0.66
37.15
±
45.58
23.85
±
27.04
38.72
±
37.14
0.05
±
0.12
0.06
±
0.14
42.42
±
13.90
46.17
±
16.05
56.76
±
17.84
51.53
±
21.41
3.39
±
5.98
8.23
±
9.42
Water tanks han-
dling
7.45
±
13.50
80.01
±
11.23
12.53
±
13.53
0.01
±
0.02
1.65
±
2.22
85.88
±
11.91
12.36
±
13.58
0.11
±
0.22
0.25
±
0.49
0.56
±
1.12
36.90
±
33.76
72.85
±
15.60
64.43
±
37.55
53.52
±
21.25
0.52
±
1.05
13.46
±
12.18
Pressure washing 1.43
±
3.01
76.70
±
16.60
17.97
±
14.25
3.90
±
6.02
3.75
±
6.37
76.35
±
14.77
16.88
±
12.33
3.02
±
4.29
0 0 25.75
±
20.94
30.52
±
33.75
53.89
±
33.84
47.56
±
9.69
0.02
±
0.05
11.29
±
17.60
Operating table
moving
2.82
±
5.63
10.48
±
19.16
21.05
±
39.37
65.66
±
37.73
0.07
±
0.14
12.79
±
23.45
13.47
±
24.52
73.67
±
27.77
0 0.38
±
0.76
44.13
±
33.34
52.06
±
38.95
46.67
±
26.51
39.72
±
20.98
20.89
±
25.60
11.93
±
8.63
Stretcher moving 0.01
±
0.02
5.70
±
8.38
49.67
±
25.96
44.62
±
22.37
0.06
±
0.15
5.95
±
8.42
45.46
±
33.15
48.53
±
29.52
0 0 36.75
±
22.29
53.99
±
25.99
53.23
±
23.46
52.98
±
23.20
0.22
±
0.37
3.36
±
4.69
Operating table
cleaning
3.23
±
4.30
72.38
±
15.88
22.14
±
13.41
2.26
±
5.54
4.98
±
6.22
82.53
±
17.03
10.05
±
10.44
2.44
±
5.98
0 0.14
±
0.35
63.01
±
20.30
52.08
±
22.65
55.64
±
18.69
36.06
±
17.41
2.66
±
6.53
25.99
±
17.11
Operating table
disassembly
2.23
±
3.23
56.28
±
17.22
22.93
±
14.80
18.56
±
19.72
3.86
±
4.12
56.65
±
17.80
18.23
±
14.47
21.26
±
16.99
0.34
±
0.59
0 36.21
±
21.99
44.85
±
10.18
51.44
±
12.93
45.57
±
12.92
2.44
±
4.41
18.50
±
12.53
Sheets moving 6.55
±
6.05
85.93
±
6.14
7.20
±
7.48
0.31
±
0.58
8.63
±
7.49
86.19
±
8.49
4.96
±
5.86
0.22
±
0.58
1.65
±
3.36
0.25
±
0.57
35.63
±
10.16
36.02
±
29.93
42.80
±
18.25
43.13
±
18.21
0.76
±
0.96
7.01
±
8.76
Trolley moving 0.33
±
0.72
14.00
±
20.36
30.84
±
31.39
54.83
±
29.51
0.35
±
0.52
12.60
±
22.27
31.49
±
28.02
55.57
±
30.74
0 0.06
±
0.13
56.57
±
19.25
63.77
±
13.70
47.91
±
14.92
47.75
±
15.01
2.32
±
2.66
6.55
±
10.05
Table 5.3: Mean percentage of time spent at each RULA range and mean percentage of time spent at
a risky level for each local RULA score per subtask
5.4 Nordic musculoskeletal questionnaire answers
Nordic musculoskeletal questionnaire answers allow us first to get physiological information
about our 9 subjects: they are 8 right-handed and 1 ambidextrous. Other physiological data is given in
section 4.1. Moreover, as summarized in tables 5.5 and 5.6, these answers also provide us information
about upper body parts where subjects suffered from disorders. We can notice that lower back is the
most frequently affected region, closely followed by neck.
36

44. Subtask Number of significant differences
Right Left
Waste disposal 10 7
Handling of lighting 5 4
Handling around the patient 6 5
Cables and pipes handling 6 4
Patient transfer 10 11
Surfaces and tools cleaning 5 5
Various objects moving 5 6
Floor cleaning 5 6
Boxes lifting 6 3
Water tanks handling 6 3
Pressure washing 5 5
Operating table moving 13 13
Stretcher moving 13 13
Operating table cleaning 5 5
Operating table disassembly 11 8
Sheets moving 9 10
Trolley moving 13 12
Table 5.4: Number of significant differences per subtask
Body region Last 7 days Last 12 months Limitation in the workday
Neck 22.22% 55.56% 22.22%
Shoulders 11.11% 44.44% 0%
Elbows 0% 11.11% 0%
Wrists 0% 44.44% 0%
Upper back 0% 33.33% 11.11%
Lower back 33.33% 66.67% 44.44%
Table 5.5: Frequencies of upper body work-related MSDs over the last 12 months and their conse-
quences
37

45. Body region Disorders Injuries Need to change jobs
Neck 66.67% 11.11% 0%
Shoulders 66.67% 0% 0%
Elbows 11.11% 0% 0%
Wrists 44.44% 22.22% 0%
Upper back 44.44% 0% 22.22%
Lower back 77.78% 22.22% 11.11%
Table 5.6: Frequencies of upper body work-related MSDs in the past and their consequences
38

46. Chapter 6
Discussion
This study aimed to conduct in-field continuous physical ergonomic assessments for hospital staff
specialized in disinfection tasks and patient displacements according to subtasks they perform in order
to prevent upper body work-related MSDs. The assessments had to be based on a system combining
the RULA method with IMUs and video recordings.
Workers kinematic data and lifted weight could be collected respectively by the IMUs and the
video analysis and they could be processed to compute RULA scores with their features: for global
scores, mean values and percentages of time spent at each RULA range; for local ones, mean values
and percentages of time spent at a risky level. Subtasks could be identified and segmented through
video analysis and then each of them could be associated to the above-mentioned RULA features.
Therefore, goals of this thesis have been achieved thanks to the study that has been conducted.
We have first assumed that average RULA scores would be associated with awkward postures.
Then, we have hypothesized that most frequently risky local scores would be related to MSDs af-
fecting specific upper body parts. Finally, we have assumed that riskiest subtasks would be the same
causes of MSDs as in the literature. The next three sections will answer these assumptions.
6.1 Main results compared to literature
Results have shown that average global RULA scores (right side: 4.21±1.15; left side: 4.19±1.20)
are closer to the range 3-4. The latter is also the range on which the biggest part of time is spent on
average (right side: 63.54±31.59%; left side: 64.33±32.33%). Thus, the average posture held by
hospital staff needs further investigation and change may be needed [McAtamney and Corlett, 1993].
This posture is hence not as awkward as previously assumed.
These global results cannot be compared to those from [Carbonaro et al., 2021] or [Maurer-Grubinger et al., 20
39

47. who dealt with hospital staff. In fact, in the former study, global scores were not computed since the
assessment was limited to neck and trunk while the latter study did not compute the same RULA
features.
Nevertheless, we can compare our results with those from [Vignais et al., 2013] and [Vignais et al., 2017],
although they did not deal with hospital staff but respectively with manufacturing workers and labora-
tory workers. Our mean values appear to be in the same range than those from subjects without RULA
feedback in the 2013 study (right side: 4.4±0.65; left side: 4.31±0.46) but much lower than those
from the 2017 study (right side: 6±0.87; left side: 6.2±0.78). As for our range on which the biggest
part of time is spent on average, it is the same than for the subjects without RULA feedback in the 2013
study, although the latter have a slightly lower percentage (mean between both sides: 56.91±13.64%)
while, in the 2017 study, most time is spent on the range 7 (right side: 49.19±35.27%; left side:
55.5±29.69%).
6.2 Connection between local scores and musculoskeletal disor-
ders
Local RULA scores have been computed in order to identify upper body areas that are more at risk.
Yet, no mean value is higher than the predefined thresholds of risky level. However, according to their
mean percentages of time spent at this level, wrists and hands (right side: 50.52±19.56%; left side:
46.95±17.80%) and elbows and lower arms (right side: 43.55±21.27%; left side: 45.37±22.27%)
appear to be more used to adopting hazardous postures than other areas.
Among nurses, work-related MSDs especially affect lower back [Carneiro et al., 2019] [Boughattas et al., 2017
[Al-samawi et al., 2015]. Yet, pelvis and trunk are not as used to adopt hazardous postures (mean
score: 2.41±0.70; mean percentage of time: 11.50±14.40%) among our subjects. However, as for the
latter, surgeons suffer, inter alia, from hand disorders [Soueid et al., 2010] while MSDs among nurs-
ing assistants affect their upper limbs, then their elbows, lower arms, wrists and hands [Ching et al., 2018].
Plus, elbows and lower arms (both sides: 100%) and wrists and hands (right side: 82.13±7.46%; left
side: 77.85±12.46%) are areas that spend the most time at a risky level in [Vignais et al., 2017] study
too.
Despite appearing as riskiest areas, elbows and wrist are not the more affected upper body parts,
according to Nordic musculoskeletal questionnaire answers. In fact, only one subject complains about
MSDs affecting the elbows and without any consequences. As for wrists, four subjects complain
about MSDs affecting them, including two who have already suffered from injuries, but without
40

48. consequences on the work.
On the other hand, lower back and neck are the most frequently affected regions, according to
Nordic musculoskeletal questionnaire answers. In fact, seven subjects complain about MSDs affect-
ing the former area, including six in the last 12 months, three in the last seven days, four getting their
workday limited, two having suffered from injuries and one who have had to change jobs. As for
the neck, six subjects complain about MSDs affecting the former area, including five in the last 12
months, two in the last seven days, two getting their workday limited and one having suffered from
injuries. Yet, according to the RULA-based experiment, pelvis and trunk spend only 11.50±14.40%
of the time at a risky level while neck and head spend only 4.44±10.88%. In a nuthsell, risky local
scores are not related to subjective data about upper-body MSDs.
6.3 Subtasks compared to literature and connection with muscu-
loskeletal disorders among hospital staff
Subtask analysis has been performed to identify risky subtasks. Those associated to the highest
global scores are ’Stretcher moving’ and ’Operating table moving’, in addition, for the latter, to spend
the most of time at a RULA range of 7. Plus, with ’Trolley moving’, they are the three subtasks whose
average scores are higher than 6 and they are those showing the most frequently significant differences
with other subtasks. However, these subtasks have neither the highest average local scores nor the
highest percentages of time at local risky levels, except ’Operating table moving’ for the neck in both
cases. This may be explained by the influence of the force/load score. In fact, these subtasks involve
displacements of high weights. It may be therefore relevant to find a way to reduce these loads.
Focusing on riskiest upper body areas, i.e. elbows and wrists, we have found subtasks with high-
est average local scores and percentages of time at a risky level are ’Floor cleaning’, ’Operating table
cleaning’ and ’Water tanks handling’ for the former and ’Water tanks handling’ and ’Patient transfer’
for the latter. As for subjective riskiest areas according to Nordic musculoskeletal questionnaire an-
swer, neck is most affected by ’Operating table moving’ while trunk is most affected by ’Operating
table cleaning’ for mean value and ’Floor cleaning’ for percentage of time. Except for ’Patient trans-
fer’, these subtasks involve handling of materials that may be modified to less solicit all these areas.
As for ’Patient transfer’, a training program such as in [Callihan et al., 2020] may be relevant.
Among various studies dealing with nurses and nursing assistants, patient lifting and handling are
frequently cited as a main cause of work-related MSDs [Callihan et al., 2020] [Boughattas et al., 2017]
[Ching et al., 2018] [Al-samawi et al., 2015]. These subtasks are equivalent to ’Patient transfer’ in
41

49. our case. Besides being one of the riskiest subtasks for wrists, its mean global scores are closest to
the range 5-6 (right side: 4.95±1.05; left side: 4.98±1.12), which implies further investigation and
change soon. However, most time of this subtask is spent in range 3-4 (right side: 44.39±35.45%;
left side: 42.69±36.65%). Finally, this subtask appears to be also risky for elbows and lower arm
according to percentages of time (right side: 55.33±31.22%; left side: 33.95±24.28%).
In a nutshell, subtasks that are significantly riskier than others are not the same than those leading
to MSDs according to literature. Nevertheless, ’Patient transfer’ is equivalent to patient handling and
lifting in the literature and it appears to be one of the riskiest subtasks for local scores.
6.4 Feedback comfort questionnaire
We can wonder about the influence of the IMUs system on the workers movements. In fact
wearing a skin-tight jacket, a headband, straps and gloves with electronic boxes under working clothes
could have led to a certain discomfort that might have disturbed them while the work was performed.
If so, our ergonomic scores might be biased by this discomfort. A feedback comfort questionnaire
about the IMUs system was then filled by the subjects after the experiment (Appendix 6). Their
answers are summarized in table 6.1.
6.5 Limitations and perspectives
First limitations are related to the physical ergonomic assessment method. In fact, the RULA
method suffers from a lack of epidemiological data assessing the relationship between high scores
computation and occurrence of MSDs [Li and Buckle, 1999] [Vignais et al., 2017]. Some studies
have sought to address this problem but they are limited to a few anatomical areas whereas the RULA
method focuses on the whole upper body [Vignais et al., 2017]. Furthermore, some conditional state-
ments are based on qualitative information rather than quantitative one. For example, if the trunk is
twisted, +1 point is added to trunk score. Yet, the method does not propose any angle threshold to
continuously assess this kind of condition. Thus, we had to subjectively set them, based on those
selected by [Vignais et al., 2017].
Besides RULA limitations, the system we have developed could not collect all the information
we needed to carry out the physical ergonomic assessment. As mentioned in section 4.4.1, shoulder
raising statement, leg score and muscle use score were indeed set by default according to our observa-
tions. While we were developping the system, we tried to compute leg score by placing IMUs placed
on the lower limbs but they appeared to be more sensitive to noise and to provide much less accurate
42

50. Question Totally agree Agree Neutral Disagree Totally disagree
XSens system
XSens is easy to put on 88.89% 0% 11.11% 0% 0%
XSens is suitable for work 88.89% 0% 11.11% 0% 0%
XSens is annoying when you move 0% 0% 33.33% 0% 66.67%
The jacket bothers you 0% 0% 33.33% 0% 66.67%
The arm and forearm straps bother
you
0% 0% 22.22% 11.11% 66.67%
The headband bothers you 0% 0% 22.22% 11.11% 66.67%
The gloves bother you 0% 0% 33.33 0% 66.67%
XSens while working
You are comfortable with XSens all
along the experiment
77.78% 0% 22.22% 0% 0%
XSens disrupts your work 0% 0% 33.33% 0% 66.67%
XSens requires extra concentration 0% 0% 22.22% 11.11% 66.67%
XSens causes you discomfort 0% 0% 33.33% 0% 66.67%
XSens inconveniences
Roughness 0%
Pressure 11.11%
Motion 22.22%
Heat 22.22%
Table 6.1: Frequencies of answers to each statement of the feedback comfort questionnaire
and precise data than those placed on the upper body. Plus, force/load score was computed accord-
ing to lifted weight. Yet, the latter was estimated through video recordings observations and was
not very accurate since not all of them are known. However, weight ranges conditioning force/load
score are quite wide: less than 2 kg, between 2 and 10 kg and more than 10 kg. Finally, wrist angle
measurements were subject to kinematic cross-talk causing their inaccuracy. For example, when a
wrist flexion was performed without any other movements, an pronation/supination and/or a radio-
ulnar deviation could be measured by the IMUs at the same time. A solution might be the use of
electrogoniometers for these measurements instead of IMUs, as employed by [Vignais et al., 2017].
The study was also limited by the low number of participants due to the human capacities of the
place where it was performed. For more robust results, it would be relevant to apply the experimental
procedure we have built to hospital staff from other surgery departments so that we could get a higher
43

51. number of participants. Moreover, among the nine participants, one had unusable data. In fact, during
the experimentation with him/her, other staff in the operating room asked us not to videotape and we
had to respect their wishes. Consequently, subtask analysis could not be performed. Inspired from
[Malaisé, 2020] thesis, we have imagined a machine learning based system that would be trained and
tested by the eight other subjects data to automatically identify subtasks performed by the unfilmed
subject and then analyze them. This idea was planned during the internship but there was not much
time left to put it into practice.
44

52. Chapter 7
Conclusion
Despite results that have not allowed us to validate our assumptions, we have reached our goal
of conducting in-field physical ergonomic assessments for hospital staff to prevent their upper body
work-related MSDs thanks to the methodology we have developed. Such a methodology therefore
deserves to be tested on a larger population of participants, with more efficient data recording and
processing chain.
Exchanges with the tested participants and hospital managers showed us to which extent advanced
ergonomic research like ours was needed to prevent work-related MSDs among healthcare personal.
Thus, a similar experiment might be conducted for other hospital professions such as operating room
nurses in the future.
On the other hand, given the societal burden of MSDs affecting hospital staff, advanced aiding
systems would certainly be welcome. Studies like ours pave the way towards the design of such
aiding systems.
45

53. Bibliography
[CNA, ] Cours - données manquantes. http://cedric.cnam.fr/vertigo/Cours/ml/
coursDonneesManquantes.html. Consulted on 08/18/2021.
[AME, 2020] (2020). Les tms : définition et impact. https://
www.ameli.fr/paris/entreprise/sante-travail/risques/
troubles-musculosquelettiques-tms/tms-definition-impact. Consulted on
09/3/2021.
[Al-samawi et al., 2015] Al-samawi, M. A. G., Ahmed, H. M., and Awad, A. (2015). Incidences of
low back pain among nurses working in elmak nimer university hospital - shendi - sudan 2015.
volume 3, pages 129–138.
[Alghadir et al., 2021] Alghadir, A. H., Al-Abbad, H., Buragadda, S., and Iqbal, A. (2021). Influence
of work-related safety and health guidelines on knowledge and prevalence of occupational back
pain among rehabilitation nurses in saudi arabia: A 6-month follow-up study. International Journal
of Environmental Research and Public Health, 18(16).
[Aptel et al., 2011] Aptel, M., Cail, F., and Aublet-Cuvelier, A. (2011). Les troubles musculosquelet-
tiques du membre supérieur (TMS-MS) - Guide pour les préventeurs.
[Battini et al., 2014] Battini, D., Persona, A., and Sgarbossa, F. (2014). Innovative real-time sys-
tem to integrate ergonomic evaluations into warehouse design and management. Computers &
Industrial Engineering, 77:1–10.
[Bernmark and Wiktorin, 2002] Bernmark, E. and Wiktorin, C. (2002). A triaxial accelerometer for
measuring arm movements. Applied Ergonomics, 33(6):541–547.
[Boughattas et al., 2017] Boughattas, W., Maalel, O., Maoua, M., Bougmiza, I., Kalboussi, H., Bra-
hem, A., Chatti, S., Mahjoub, F., and Mrizak, N. (2017). Low back pain among nurses: Prevalence,
and occupational risk factors. Occupational Diseases and Environmental Medicine, 5(1):26–37.
46

54. [Burton, 1986] Burton, A. (1986). Regional lumbar sagittal mobility: measurement by flexicurves.
Clinical Biomechanics, 1(1):20–26.
[Callihan et al., 2020] Callihan, M., Eyer, J., McCoy, C., Dailey, A. M., Diket, K. M., Robinson,
A., and Kaylor, S. (2020). Development and feasibility testing of a contextual patient movement
intervention. Journal of emergency nursing.
[Carbonaro et al., 2021] Carbonaro, N., Mascherini, G., Bartolini, I., Ringressi, M. N., Taddei, A.,
Tognetti, A., and Vanello, N. (2021). A wearable sensor-based platform for surgeon posture mon-
itoring: A tool to prevent musculoskeletal disorders. International Journal of Environmental Re-
search and Public Health, 18(7).
[Carneiro et al., 2019] Carneiro, P., Villarroya, A., Colim, A., Torres, M., and Arezes, P. (2019).
Ergonomic Study of Nursing Tasks in Surgical Hospital Services, pages 29–36. Health and Social
Care Systems of the Future: Demographic Changes, Digital Age and Human Factors.
[Ching et al., 2018] Ching, S. S. Y., Szeto, G., Lai, G. K. B., Lai, X. B., Chan, Y. T., and Cheung,
K. (2018). Exploring the synergic effects of nursing home work on work-related musculoskeletal
disorders among nursing assistants. Workplace Health & Safety, 66(3):129–135. PMID: 28770660.
[Christmansson, 1994] Christmansson, M. (1994). Repetitive and manual jobs—content and effects
in terms of physical stress and work-related musculoskeletal disorders. International Journal of
Human Factors in Manufacturing, 4(3):281–292.
[Collins et al., 2006] Collins, J., Nelson, A., and Sublet, V. (2006). Safe Lifting and Movement of
Nursing Home Residents. National Institute for Occupational Safety and Health, 4676 Columbia
Parkway, Cincinnati, Ohio 45226-1998, niosh-publications dissemination edition.
[David, 2005] David, G. (2005). Ergonomic methods for assessing exposure to risk factors for work-
related musculoskeletal disorders. Occupational medicine (Oxford, England), 55:190–9.
[Dianat et al., 2018] Dianat, I., Bazazan, A., Souraki Azad, M. A., and Salimi, S. S. (2018). Work-
related physical, psychosocial and individual factors associated with musculoskeletal symptoms
among surgeons: Implications for ergonomic interventions. Applied Ergonomics, 67:115–124.
[Fransson-Hall et al., 1995] Fransson-Hall, C., Gloria, R., Kilbom, A., Winkel, J., Karlqvist, L., and
Wiktorin, C. (1995). A portable ergonomic observation method (peo) for computerized on-line
recording of postures and manual handling. Applied Ergonomics, 26(2):93–100.
47

55. [Garoche, 2016] Garoche, B. (2016). L’exposition des salariés aux maladies professionnelles en 2012
- l’importance toujours prépondérante des troubles musculo-squelettiques. DARES Résultats, (81).
[Hansson et al., 2001] Hansson, G., Asterland, P., Holmer, N., and Skerfving, S. (2001). Validity and
reliability of triaxial accelerometers for inclinometry in posture analysis. Medical and Biological
Engineering and Computing, (39):405–413.
[Huang et al., 2020] Huang, C., Kim, W., Zhang, Y., and Xiong, S. (2020). Development and val-
idation of a wearable inertial sensors-based automated system for assessing work-related muscu-
loskeletal disorders in the workspace. International Journal of Environmental Research and Public
Health, 17(17).
[Kadefors and Forsman, 2000] Kadefors, R. and Forsman, M. (2000). Ergonomic evaluation of com-
plex work: a participative approach employing video–computer interaction, exemplified in a study
of order picking. International Journal of Industrial Ergonomics, 25(4):435–445.
[Karhu et al., 1977] Karhu, O., Kansi, P., and Kuorinka, I. (1977). Correcting working postures in
industry: A practical method for analysis. Applied Ergonomics, 8(4):199–201.
[Kemmlert, 1995] Kemmlert, K. (1995). A method assigned for the identification of ergonomic haz-
ards — plibel. Applied Ergonomics, 26(3):199–211.
[Kuorinka et al., 1987] Kuorinka, I., Jonsson, B., Kilbom, A., Vinterberg, H., Biering-Sørensen, F.,
Andersson, G., and Jørgensen, K. (1987). Standardised nordic questionnaires for the analysis of
musculoskeletal symptoms. Applied Ergonomics, 18(3):233–237.
[Li and Buckle, 1998] Li, G. and Buckle, P. (1998). A practical method for the assessment of work-
related musculoskeletal risks - quick exposure check (qec). Proceedings of the Human Factors and
Ergonomics Society Annual Meeting, 42(19):1351–1355.
[Li and Buckle, 1999] Li, G. and Buckle, P. (1999). Current techniques for assessing physical expo-
sure to work-related musculoskeletal risks, with emphasis on posture-based methods. Ergonomics,
42(5):674–695. PMID: 10327891.
[Malaisé, 2020] Malaisé, A. (2020). Apprentissage du mouvement humain à l’aide de capteurs portés
: vers l’automatisation de l’évaluation ergonomique. PhD thesis, Université de Lorraine.
[Marras et al., 1992] Marras, W., Fathallah, F., Miller, R., Davis, S., and Mirka, G. (1992). Accuracy
of a three-dimensional lumbar motion monitor for recording dynamic trunk motion characteristics.
International Journal of Industrial Ergonomics, 9(1):75–87.
48

56. [Maurer-Grubinger et al., 2021] Maurer-Grubinger, C., Holzgreve, F., Fraeulin, L., Betz, W., Erbe,
C., Brueggmann, D., Wanke, E. M., Nienhaus, A., Groneberg, D. A., and Ohlendorf, D. (2021).
Combining ergonomic risk assessment (rula) with inertial motion capture technology in den-
tistry—using the benefits from two worlds. Sensors, 21(12).
[Maurice, 2015] Maurice, P. (2015). Virtual ergonomics for the design of collaborative robots. PhD
thesis, Université Pierre et Marie Curie.
[McAtamney and Corlett, 1993] McAtamney, L. and Corlett, E. N. (1993). Rula: a survey method
for the investigation of work-related upper limb disorders. Applied Ergonomics, 24(2):91–99.
[Mcatamney and Hignett, 1995] Mcatamney, L. and Hignett, S. (1995). Reba: A rapid entire body
assessment method for investigating work related musculoskeletal disorders. Proceedings of the
31st Annual Conference of the Ergonomics Society of Australia, pages 45–51.
[Paulich et al., 2018] Paulich, M., Schepers, M., Rudigkeit, N., and Bellusci, G. (2018). Xsens MTw
Awinda: Miniature Wireless Inertial-Magnetic Motion Tracker for Highly Accurate 3D Kinematic
Applications. XSENS TECHNOLOGIES B.V.
[Plantard et al., 2017] Plantard, P., Shum, H. P., Le Pierres, A.-S., and Multon, F. (2017). Valida-
tion of an ergonomic assessment method using kinect data in real workplace conditions. Applied
Ergonomics, 65:562–569.
[Pope et al., 1998] Pope, D. P., Silman, A. J., Cherry, N. M., Pritchard, C., and Macfarlane, G. J.
(1998). Validity of a self-completed questionnaire measuring the physical demands of work. Scan-
dinavian Journal of Work, Environment & Health, (5):376–385.
[Priel, 1974] Priel, V. Z. (1974). A numerical definition of posture. Human Factors, 16(6):576–584.
PMID: 4442902.
[Soueid et al., 2010] Soueid, A., Oudit, D., Thiagarajah, S., and Laitung, G. (2010). The pain
of surgery: Pain experienced by surgeons while operating. International Journal of Surgery,
8(2):118–120.
[Spielholz et al., 1999] Spielholz, P., Silverstein, B., and Stuart, M. (1999). Reproducibility of a
self-report questionnaire for upper extremity musculoskeletal disorder risk factors. Applied Er-
gonomics, 30(5):429–433.
49

57. [Vignais et al., 2017] Vignais, N., Bernard, F., Touvenot, G., and Sagot, J.-C. (2017). Physical risk
factors identification based on body sensor network combined to videotaping. Applied ergonomics,
65.
[Vignais et al., 2013] Vignais, N., Miezal, M., Bleser, G., Mura, K., Gorecky, D., and Marin, F.
(2013). Innovative system for real-time ergonomic feedback in industrial manufacturing. Applied
Ergonomics, 44(4):566–574.
[Viikari-Juntura et al., 1996] Viikari-Juntura, E., Rauas, S., Martikainen, R., Kuosma, E., Riihimäki,
H., Takala, E.-P., and Saarenmaa, K. (1996). Validity of self-reported physical work load in epi-
demiologic studies on musculoskeletal disorders. Scandinavian Journal of Work, Environment &
Health, (4):251–259.
[Wells et al., 1994] Wells, R., Moore, A., Potvin, J., and Norman, R. (1994). Assessment of risk
factors for development of work-related musculoskeletal disorders (rsi). Applied Ergonomics,
25(3):157–164.
[Wiktorin et al., 1995] Wiktorin, C., Mortimer, M., Ekenvall, L., Kilbom, A., and Wigaeus Hjelm, E.
(1995). Harbo, a simple computer-aided observation method for recording work postures. Scandi-
navian Journal of Work, Environment & Health, (6):440–449.
50