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Virtual Reality Serious Game for
Musculoskeletal Disorder prevention
Maria Sisto1, Mohsen Zare2, Nabil Ouerhani3, Christophe Bolinhas1, Margaux
Divernois3, Bernard Mignot2, Jean-Claude Sagot2, and St´ephane Gobron1
1Image Processing and Computer Graphics group, HE-Arc, HES-SO, Switzerland
2ERCOS Group (pole), Laboratory of ELLIAD-EA4661, UTBM-University of
Bourgogne Franche-Comt, France
3Interaction Technology group, HE-Arc, HES-SO, Switzerland
Abstract. Musculo Skeletal Disorders (MSDs) is the most common dis-
ease in the workplaces causing disabilities and excessive costs to indus-
tries, particularly in EU countries. Most of MSDs prevention programs
have focused on a combination of interventions including training to
change individual behaviors (such as awkward postures). However, little
evidence proves that current training approach on awkward postures is
efficient and can significantly reduce MSDs symptoms. Therefore, deal-
ing with awkward postures and repetitive tasks is the real challenge for
practitioners and manufacturers, knowing that the amount of risk expo-
sure varies increasingly among workers depending on their attitude and
expertise as well as on their strategy to perform the task. The progress in
MSDs prevention might come through developing new tools that inform
workers more efficiently on their gestures and postures. This paper pro-
poses a potential Serious Game that immerses industrial workers using
Virtual Reality and helps them recognize their strategy while performing
tasks and trains them to find the most efficient and least risky tactics.
Keywords: Serious Game; Musculoskeletal Disorder; Health application; Vir-
tual Reality; Virtual environment; Head-mounted display;
1 Introduction
Musculoskeletal Disorders (MSDs) are the most common work-related injuries
in Western countries, reaching up to 2% of the gross domestic product (GPD) in
European countries. In France, for example, MSDs represent more than 87% of
all the occupational diseases in 2016 [1]. In Europe, millions of people suffer from
MSD-associated diseases [2, 3], which impact the workers’ life quality and have
a substantial economic and social burden on companies and communities [2].
Absenteeism, permanent disability, compensation and medical expenses are the
main, tangible consequences while intangible costs of MSDs (such as a family in-
come shortage, mental damage in the workplace, and loss of skillful workforces)
are less considered and might have more social and economic effects [4,5]. Recent
2
Fig. 1. Concept image of a user playing the game. A feedback is then given on its
posture (upper right).
etiological MSDs risk models have well established the role of physical, organi-
zational and psychosocial risk factors in the MSDs development [6,7]. Although
all those factors interact with each other, the physical risk factor - particularly
awkward and static postures - remains the most hazardous element of industrial
tasks [8].
Many ergonomic intervention studies have focused on reducing hazardous
tasks by job redesigning, training workers and participation of stakeholders
(especially workers). Ergonomic interventions are mostly implemented through
the use of new equipment (like lifting tools), staff training and organizational
changes [9–11]. Most of these studies report workers’ participation and train-
ing as a key element for the intervention success [12–15]. However, several re-
cent studies questioned the effectiveness of ergonomic interventions and showed
that classic physical and organizational ergonomic interventions have low effi-
ciency [16–18].
Classic training programs (such as lectures, on-site training, posters and
brochures) are insufficient to change workers behaviors and improve their pos-
tures [11, 19, 20]. Although some studies have found classic ergonomic training
to be significantly effective in the short term, sustainability of posture and be-
havior changes was reported in very few cases [15, 21, 22], the post-intervention
effects of training often reducing rapidly after several months. Therefore, man-
ufacturing industries need new methods, which not only involve active workers
3
training to improve postures and behaviors [12, 13, 20] but also provide a sus-
tainable approach in the prevention of MSDs. With this study we want to know
whether modern technologies such as Virtual Reality (VR) could contribute to a
sustainable MSD prevention. A Serious Game (SG) is introduced as a potential
option to provide a sustainable training and behavior changes, particularly in
MSD care. Although these technologies are a novelty in MSD prevention, serious
games as a pedagogic tool for health and safety purposes have already been used
in previous studies [23–25]. We also want to know whether a fully immersive en-
vironment, which provides feedback on the users MSD risk exposure, contributes
to MSD prevention in manufacturing industries (see Figure 1).
More specifically, this paper wants to propose a SG that combines VR and
Motion Capture (MoCap) to prevent work-related MSDs. We hypothesize that
an attractive SG increases the workers’ knowledge of MSD risk factors and helps
them develop new strategies to reduce risk exposure. That’s why we proposed an
innovative tool as a possible alternative to replace the classic training approach.
2 State of the art
2.1 MSDs Prevention
Ergonomic interventions focusing on MSDs risk factors such as static and awk-
ward postures, frequent bending and twisting, and repetitive work could reduce
the risk of MSDs. However, the various components of these interventions are
difficult to implement [26]. Recent studies propose a combination of ergonomic
interventions to reduce MSDs symptoms: job redesign, technical modifications,
ergonomic training, postural advice and organizational changes as the most com-
mon interventions used to reduce MSDs symptoms [10,12,16]. As industrial work-
ers are directly involved in work and influenced by MSD risk factors, ergonomic
worker-targeted interventions (for example, physical exercise program, training,
ergonomic advice and instruction on working methods) are mostly integrated
into the ergonomic intervention studies [15, 16, 21]. Health promotion actions
at the workplace are similarly used to inform workers of good practices and
promote preventive interventions at work (e.g., stretching program) or at home
(e.g. diet or exercise programs) [27]. However, recent high-quality studies did
not confirm the effectiveness of interventions (particularly training and postural
advice) in behavior changes [11,19, 22,28]: McDermott et al. (2012) investigated
the practical implementation of manual material handling training within 150
industry sectors in the UK and concluded that training is more efficient when
adapted to a specific task or job. Although the majority of industries currently
propose regular training based on legislation, the training efficiency remains
unknown. Systematic reviews showed inconsistent and insufficient evidence to
conclude that current training approach is effective to reduce awkward postures
and MSDs [19,29]. Changing workers behaviors (such as awkward postures and
non-adapted strategies in performing a task) based on current training approach
(general lectures and classroom-based activities) is a challenge for MSDs preven-
tion. The novelty in MSDs prevention through training would be to develop a
4
new tool that could suppress the deficiencies of current training approaches. An
sector-adapted training approach that implies training a worker in a familiar
task might improve training efficiency and achieve successful behavior changes.
VR and game technologies are new tools that might change workers behaviors in
a playful setting and we believe that they can increase the intervention success
and reduce awkward postures by providing an efficient training.
2.2 Serious Games for MSD prevention
Many SGs for the rehabilitation of those already suffering from MSDs exist [30–
33], but few SGs focus on MSD prevention. A French company (daesign [34])
conceived a MSD prevention SG that is meant as a one-time use. It contains a
little of gamification by means of quizzes, an interactive desk and a ”find the
mistakes” game. The other SGs focus on encouraging stretching exercises as a
specific MSD prevention technique. Motion tracking is used to validate the user’s
action. Rodrigues et al. have developed a SG joining the stretching and game
phase where the user must correct the posture of some virtual workers [35,36].
Freitas et al. did a series of mini-games that focused on the hands and used
stretching exercises as an input for the game [37]. All those games target office
workers and we need a SG that targets the occupational risk factors in factories.
2.3 Prevention, training and Virtual Reality
Even if there are few SGs on MSDs, SGs have largely been used in the rehabili-
tation and health fields [38] often paired with motion tracking or haptic devices
and have proven to be efficient [38]. Moreover, it has been proven that Virtual
Reality (VR) helps teach assembly tasks/procedure sequences [39] and is often
used as a support for immersive SGs [40]. SGs have even been used with VR
and Motion Capture in a prevention game [41], but not for MSD prevention.
2.4 Our approach
This study proposes a new approach to MSDs prevention, with a SG focusing
on real-life movements and applied to industrial workers. We used the differ-
ent workers’ strategies to develop a SG that combines VR and Motion capture
and provides real-time feedback on the user’s awkward postures. This SG is de-
contextualized in virtual reality to get rid of the effects of the environmental
components that play a role in MSDs.
3 Data collection in real industrial settings
3.1 Data acquisition
To develop a game close to real work settings, we first created a database of
the postures and movements (biomechanical data) of industrial tasks selected
5
from different workstations in the automotive and watchmaking industries. We
chose four sectors of the automobile industry (namely injection press, paint-
ing, change parts and bumper assembly) and four operations in watchmaking
(placing watch dial, setting watch hands, casing movement and visiting). The
principal tasks from the automobile industry were packing the bumper and its
small parts, preparing, painting and assembling different types of car bumpers.
The watchmaker first puts manually the dial on the movement and then fits the
hands to the right height and correct position. The operator cases the movement
after cleaning the glass and closes the case back. We decided to include these
tasks in our study after several visits to the workstations and discussions with
industrial stakeholders. These tasks seemed to be more appropriate to develop
game scenarios. Twenty automobile assembly operators (8 women and 12 men)
and twelve women watchmakers accepted to participate in this experiment. They
had a good physical condition without health problems or distress. Most of the
participants were polyvalent and could work on several workstations allowing us
to measure their postures and movements in different situations. We used nine
light T-motion sensors (32 g, 60 35 19 mm) to continuously measure the upper
limb joint angles at a 64 Hz frequency. Each sensor includes a triaxial gyro-
scope (it measures angular velocity in degrees/sec), a triaxial accelerometer (it
measures linear acceleration in m/s2), and a triaxial magnetometer (it measures
magnetic field strength in uT). The T-motion sensors were set by adjustable
straps on the head, thorax (fixed on the back), pelvis (located on the hip bone),
arms and wrists according to the previous literature [42] and the instructions
of the manufacturer (TEA, Nancy, France). The participant was equipped in a
separate room near the workstation, then T-motion sensors were set at zero in a
reference position. The anatomical reference position is described as the human
body upright, feet close together, arms to the side and palms facing inward [43].
These reference positions and the relaxed position of the operator were registered
at the start and the end of each experiment. Two cameras filmed simultaneously
both sides of the worker. We registered ten cycle times of the subjects’ activity
after they got accustomed to the devices placed on their body and the camera
installed near them (5 minutes).
3.2 Activity analysis and data treatment
An experienced researcher in ergonomics and job analysis manually coded the
videotaping-recorded activity in CAPTIV software. The subtasks were identified,
as far as possible, thanks to the job descriptions provided by the companies. On
the basis of the biomechanical model developed by the motion capture system
developer (TEA, Nancy, France), we calculated the head, upper arms, forearms,
wrists and lower back joint angles in 20 degrees of freedom. The data were
synchronized with the subtasks identified in the videos. Each subtask finally
had a precise measurement of the upper limbs and trunk joint angles. A global
risk score was calculated for each subtask based on the algorithm of the Rapid
Upper Limb Assessment (RULA) method [44]. This algorithm generates a single
6
score for each subtask which represents the MSDs risk level (score 1-2: negligible
risk, score 3-4: medium risk, score 5-7: high risk).
4 User posture and gesture acquisition
4.1 Upper body tracking for coarse posture acquisition
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15
19
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17
23
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16
19
20
3
0
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KINECT V2 JOINT ID MAP
JOITTYPE_SPINEBASE = 0.
JOITTYPE_SPINEMID = 1.
JOITTYPE_NECK = 2.
JOITTYPE_HEAD = 3.
JOITTYPE_SOULDERLEFT = 4.
JOITTYPE_ELBOWLEFT = 5.
JOITTYPE_WRISTLEFT = 6.
JOITTYPE_HANDLEFT = 7.
JOITTYPE_SHOULDERRIGHT = 8.
JOITTYPE_ELBOWRIGHT = 9.
JOITTYPE_WRISTRIGHT = 10.
JOITTYPE_HANDRIGHT = 11.
JOITTYPE_HIPLEFT = 12.
JOITTYPE_KNEELEFT = 13.
JOITTYPE_ANKLELEFT = 14.
JOITTYPE_FOOTLEFT = 15.
JOITTYPE_HIPRIGHT = 16.
JOITTYPE_KNEERIGHT = 17.
JOITTYPE_ANKLERIGHT = 18.
JOITTYPE_FOOTRIGHT = 19.
JOITTYPE_SPINESHOULDER = 20.
JOITTYPE_HANDTIPLEFT = 21.
JOITTYPE_THUMBLEFT = 22.
JOITTYPE_HANDTIPRIGHT = 23.
JOITTYPE_THUMBRIGHT = 24.
25 JOINTS
6 BODIES
Fig. 2. Human body joints captured by Kinect.
In this project context, the main goal of posture and gesture acquisition is to
provide an objective and systematic assessment of the trainee posture in regards
to widely-used ergonomic standards. The objective assessment feeds into the
Serious Game in the form of a score that gives feedback to the user on his posture
and gestures ergonomics. To do so, a system combining coarse and fine gesture
acquisition was set up, tested and tuned. An posture and gesture assessment
tool based on the Rapid Upper Limb Assessment (RULA) methodology was
then developed.
Furthermore, the acquired 3D fine and coarse body motions were used to
animate the avatar in the Serious Game presented in the next Chapter.
In many industry and work situations, operators suffer from upper body
(shoulder, neck, arms, etc.) MSDs due inadequate postures and motions while
executing repetitive tasks. That’s why the posture and gestures assessment at
the upper-body level is crucial.
7
The system that has been developed for the acquisition of upper-body ges-
tures and motion is based on Microsoft Kinect 2. Kinect provides the 25 joint
angles of the human body as shown in Figure 2. Most of these angles have been
used as provided by the Kinect SDK for posture assessment. Specific rule-based
post-processing algorithm for shoulder joints has been implemented to resolve
shoulder joint orientation ambiguity [45].
Spatio-temporal smoothing filters have been applied to the extracted silhou-
ette in order to avoid the flickering effect [46].
Fig. 3. Human posture assessment using RULA. a) You are not correctly seated, b)
You are seated at 90◦.
4.2 Hand and fingers tracking
Fine hand and finger movements are of high interest to assess operators’ gesture
and posture in high-precision manipulation like in watchmaking industry. The
LeapMotion device is used to acquire relevant information about hand posture
and gestures: parameters like hand rotation angle, finger flexion and extension
angles, computed from the LeapMotion phalanx data (Bones), are extracted.
4.3 Gesture assessment with RULA
In this project, RULA [47] is used for data analysis of on-site capture data (see
subsection 3.2), but also to assess the user’s movement during the game. To do so,
8
we developed a tool that automates RULA based on the acquired 3D posture and
gesture of human bodies. For each acquired joint we implemented an assessment
rule regarding the joint angle and orientation. The different assessment rules
have been provided by ergonomic experts. The Figure 3 illustrates a posture
assessment based on two colors: green for safe posture and red for risky posture
according to ergonomic rules and recommendations.
4.4 Integration of fine and coarse motions for avatar animation
The Serious Game described in the next Chapter includes an avatar that mimics
the user gestures and movements. On the one hand, the Kinect is used to capture
and forward the coarse body gestures and movements to the avatar animation
module. On the other hand, the LeapMotion is used to capture and forward the
fine gestures and movements. To have consistencies of both types of gestures
and motions, the hand and body 3D data should be aligned. In this project, we
implemented a simple, yet robust alignment of Kinect and LeapMotion 3D data
using geometric transformations. The Figure 4 illustrates, through a test avatar
arm, the result of this alignment.
Fig. 4. Illustration of the hand-arm integration by combining Kinect and LeapMotion
data.
5 Serious game design and scenario
5.1 Scenario
To define a game concept and scenario, we had to study the output of the mea-
sures taken in the industry workshops. To stay as close as possible to real work
9
Fig. 5. Figure illustrating the application including the hardware and the game. a)
Oculus Rift Headset and sensors, b) Leap Motion controller (head mounted), c) Kinect
V2, d) Virtual game.
situations, the game must replicate real life posture and gesture configuration .
We had to avoid the pitfall of losing the game part by making an application
which is more a gamified simulation than a game [48]. This means that the game
should not represent the objects used in the workplace and that the environment
has to be decontextualized. Furthermore, one of the significant challenges is the
fact that the game should be scalable in order to adapt to industries handling
big pieces (as in the automotive industry) and small ones (as in the watchmak-
ing industry). It should also include the option to be standing vertically or lay
horizontally on a flat surface. To sum up, these are the constraints:
– The game must induce movements close to the ones performed in the work-
place;
– The game must propose an environment and activity that differ from the
real work ones;
– The task must be scalable in size to fit different industries;
– The task must allow horizontal or vertical layout.
To meet these requirements, we imagined a puzzle-like game (see Figure 1 and
Figure 5), using gears that have to be correctly aligned to adapt to different
10
industries (gears can easily be reduced or enlarged). The user has a board with
some gears already placed and empty gear spaces. He must complete the puzzle
by placing gears in the right place on the board. At the end of the level, a score
is displayed, informing the user of the body parts that are most at risk with
MSD according to the actions he took during the session.
5.2 Environment
In a preliminary study on this project [49], it was found that the environment
impact is important and that the environment can be changed without disturbing
the user in his tasks. To allow future inclusion of these transitions in our scenario,
the user was put in a spaceship (see Figure 6). This leaves a lot of freedom with
the game physics or objects apparition and disappearing. More specifically, in the
environment transition, it is possible to put the user in a ”holographic room”
and change the holographic environment at each new level. These transitions
have not been implemented yet, but the environment has already been chosen
to ease future work.
Fig. 6. Spaceship environment.
5.3 Level design
Levels One important thing in building the levels is to balance the cognitive
load with the game difficulty. If the cognitive load is too high, the user will not
be able to learn the movements, as he will be too taken by the game. But if
the cognitive load is too low, the user will be bored and lose motivation and
engagement in the game. As it is a Serious Game, we do not want to forget the
primary aim, which is MDS prevention. We give feedback to the user at each
level validation to incorporate MSD into the game. He gets a global MSD score
11
and a detailed score on the problem he encountered. If the MSD score is too low,
the user has to do the puzzle again.
Time The time is a classical game-play element and a normal constraint in
companies, so it is natural to include it in our game. However, it must be used
with caution. In fact, introducing time constraints too early in the game will
only stress the user. Our primary aim is to teach him to do the things right
without rushing or making harmful movements. As it is not the main objective
of the game, time will impact the score in the first levels but will not prevent
the user from going to the next level.
6 Results
Fig. 7. One level of the game. a) Starting point, b) One gear has been placed, c) Two
gears have been placed, d) All gears have been placed, the level is finished.
6.1 Gear-based Serious Game
As described in the previous chapter, the chosen game is a gear puzzle game. To
avoid too high cognitive load, the different gears are color-coded. Each missing
gear has its colored axle already on the board and each level contains exactly
the right number of gears to complete the puzzle. The user must pick the right
gear and put it on its axle. If the gear is released close enough to its intended
12
place, it will automatically set correctly. The gear at this point cannot be picked
up. Once all the gears are in place, the system begins to rotate and the user can
proceed to the next level. Figure 7 describes the different phases of solving the
puzzle.
Fig. 8. Upper part: user in action on the left with the avatar animated on the right.
Lower part: User in action with the first-person view in the corner.
6.2 First users in action
The Kinect and Leap Motion are used to animate the users’ avatar and allow the
wanted interactions with the environment elements (see the setup in Figure 5).
The body is tracked, represented in the game and aligned with the camera. To
grab the gears, the user can use his own hands.
13
6.3 Scoring
The scoring in a serious game is important. The main scoring component is
the MSD score, computed by analyzing the user’s movements and postures. If
the MSD score is too low, the user has to repeat the level. Time is the other
score component, which allows the user to have bonus points if the levels are
completed within a defined time frame. At the end of every level the user gets
feedback, letting him know which articulation has been at risk during his past
actions. The global RULA score is also displayed on a scale from one to seven
(see Figure 9).
Fig. 9. Scoring board. On the left, the body is represented with a risk evaluation for
each joint. The RULA Score is displayed on the top right and the game score, solely
based on the time, on the top middle.
7 Discussion
7.1 Conclusion
This paper has investigated how the game technologies (SG and VR) as innova-
tive tools can provide an alternative for MSDs prevention. We hypothesize that
SGs have a significant potential to increase workers’ awareness in MSDs risks
prevention. A data acquisition campaign was first conducted in several facto-
ries, highlighting the most problematic situations in regards to ergonomic risks.
14
Based on these results, a SG was developed, capturing the user’s real postures,
analyzing them and producing feedback in the form of a MSD score. To enforce
good practices, the user must have a passing score to access the next game level.
7.2 Perspectives
The first results of this project are promising, as the problematic movements for
the target industries have been identified and the positions in-game by scenario
and object placement. The next step will be to provide seated levels simulating all
the positions requiring a real table and chair. So, after letting the user adjust the
table and chair as he wants, we will provide him with a feedback. Transitional
environments are one of the main features we are currently including in our
application [49], allowing a smooth transition from a virtual, learning-adapted
and calm environment to a realistic work environment.
Acknowledgment
This project was supported by the EU Interreg program, grant number 1812 as a
part of the Serious Game for Health at Work towards Musculoskeleta Disorders
(SG4H@W>MSD) project. The authors would like to thank Sebastien Chevriau,
Maxime Larique, et Gerard Touvenot for their contribution in data collection.
References
1. CNAMTS: Cour des comptes, Rapport public thmatique : La gestion du risque
accidents du travail et risques professionnels. annuel, caisse nationale dassurance
maladie des travailleurs salaris, Paris (2016)
2. Bevan, S.: Economic impact of musculoskeletal disorders (MSDs) on work in Eu-
rope. Best Practice & Research Clinical Rheumatology 29(3), 356–373 (2015).
https://doi.org/10.1016/j.berh.2015.08.002
3. Perruccio, A.V., Yip, C., Badley, E.M., Power, J.D.: Musculoskeletal Dis-
orders: A Neglected Group at Public Health and Epidemiology Meet-
ings? American Journal of Public Health 107(10), 1584–1585 (oct 2017).
https://doi.org/10.2105/AJPH.2017.303990
4. Rezagholi, M., Bantekas, A.: Making Economic Social Decisions for Improving
Occupational Health A Predictive Cost-Benefit Analysis. Occupational Medicine
& Health Affairs 03(06) (2015). https://doi.org/10.4172/2329-6879.1000225
5. Rezagholi, M.: Differential Socio-Economic Effects of Work Environmental Risk
Factors. Journal of Health & Medical Economics 2(2), 1–8 (2016)
6. Widanarko, B.: Interaction between physical and psychosocial work risk factors for
low back symptoms. Ph.D. thesis (2013)
7. Karsh, B.T.: Theories of work-related musculoskeletal disorders: Implications for
ergonomic interventions. Theoretical Issues in Ergonomics Science 7(1), 71–88 (jan
2006). https://doi.org/10.1080/14639220512331335160
8. Takala, E.P., Pehkonen, I., Forsman, M., Hansson, G.A., Mathiassen, S.E., Neu-
mann, W.P., Sjøgaard, G., Veiersted, K.B., Westgaard, R.H., Winkel, J.: Sys-
tematic evaluation of observational methods assessing biomechanical exposures at
work. Scandinavian journal of work, environment & health 36(1), 3–24 (jan 2010)
15
9. Daniels, K., Gedikli, C., Watson, D., Semkina, A., Vaughn, O.:
Job design, employment practices and well-being: a systematic re-
view of intervention studies. Ergonomics 60(9), 1177–1196 (Sep 2017).
https://doi.org/10.1080/00140139.2017.1303085
10. Sultan-Ta¨ıeb, H., Parent-Lamarche, A., Gaillard, A., Stock, S., Nicolakakis, N.,
Hong, Q.N., Vezina, M., Coulibaly, Y., V´ezina, N., Berthelette, D.: Economic eval-
uations of ergonomic interventions preventing work-related musculoskeletal disor-
ders: a systematic review of organizational-level interventions. BMC Public Health
17(1), 935 (dec 2017). https://doi.org/10.1186/s12889-017-4935-y
11. Hoe, V.C., Urquhart, D.M., Kelsall, H.L., Sim, M.R.: Ergonomic design and
training for preventing work-related musculoskeletal disorders of the upper
limb and neck in adults. Cochrane Database of Systematic Reviews (2012).
https://doi.org/10.1002/14651858.CD008570.pub2
12. Rivilis, I., Van Eerd, D., Cullen, K., Cole, D.C., Irvin, E., Tyson, J., Ma-
hood, Q.: Effectiveness of participatory ergonomic interventions on health out-
comes: A systematic review. Applied Ergonomics 39(3), 342–358 (may 2008).
https://doi.org/10.1016/j.apergo.2007.08.006
13. van Eerd, D., Cole, D., Irvin, E., Mahood, Q., Keown, K., Theberge, N., Village,
J., St. Vincent, M., Cullen, K.: Process and implementation of participatory er-
gonomic interventions: a systematic review. Ergonomics 53(10), 1153–1166 (oct
2010). https://doi.org/10.1080/00140139.2010.513452
14. Nastasia, I., Coutu, M.F., Tcaciuc, R.: Topics and trends in research on non-
clinical interventions aimed at preventing prolonged work disability in workers
compensated for work-related musculoskeletal disorders (WRMSDs): a systematic,
comprehensive literature review. Disability and Rehabilitation 36(22), 1841–1856
(oct 2014). https://doi.org/10.3109/09638288.2014.882418
15. Aghilinejad, M., Kabir-Mokamelkhah, E., Labbafinejad, Y., Bahrami-Ahmadi, A.,
Hosseini, H.R.: The role of ergonomic training interventions on decreasing neck
and shoulders pain among workers of an Iranian automobile factory: a randomized
trial study. Medical journal of the Islamic Republic of Iran 29, 190 (2015)
16. Driessen, M.T., Proper, K.I., van Tulder, M.W., Anema, J.R., Bongers, P.M.,
van der Beek, A.J.: The effectiveness of physical and organisational er-
gonomic interventions on low back pain and neck pain: a systematic re-
view. Occupational and Environmental Medicine 67(4), 277–285 (Apr 2010).
https://doi.org/10.1136/oem.2009.047548
17. Gupta, N., W˚ahlin-Jacobsen, C.D., Abildgaard, J.S., Henriksen, L.N., Nielsen,
K., Holtermann, A.: Effectiveness of a participatory physical and psychoso-
cial intervention to balance the demands and resources of industrial workers.
Scandinavian Journal of Work, Environment & Health 44(1), 58–68 (2018).
https://doi.org/10.5271/sjweh.3689
18. Driessen, M.T., Proper, K.I., Anema, J.R., Knol, D.L., Bongers, P.M., van der
Beek, A.J.: Participatory ergonomics to reduce exposure to psychosocial and phys-
ical risk factors for low back pain and neck pain: results of a cluster randomised
controlled trial. Occupational and Environmental Medicine 68(9), 674–681 (sep
2011). https://doi.org/10.1136/oem.2010.056739
19. Hogan, D.A.M., Greiner, B.A., O’Sullivan, L.: The effect of manual handling train-
ing on achieving training transfer, employee’s behaviour change and subsequent re-
duction of work-related musculoskeletal disorders: a systematic review. Ergonomics
57(1), 93–107 (2014). https://doi.org/10.1080/00140139.2013.862307
16
20. Yu, W., Yu, I.T.S., Wang, X., Li, Z., Wan, S., Qiu, H., Lin, H., Xie, S., Sun, T.: Ef-
fectiveness of participatory training for prevention of musculoskeletal disorders: a
randomized controlled trial. International Archives of Occupational and Environ-
mental Health 86(4), 431–440 (May 2013). https://doi.org/10.1007/s00420-012-
0775-3
21. Mehrparvar, A.H., Heydari, M., Mirmohammadi, S.J., Mostaghaci, M., Davari,
M.H., Taheri, M.: Ergonomic intervention, workplace exercises and musculoskeletal
complaints: a comparative study. Medical Journal of the Islamic Republic of Iran
28, 69 (Jul 2014)
22. Shuai, J., Yue, P., Li, L., Liu, F., Wang, S.: Assessing the effects of an educational
program for the prevention of work-related musculoskeletal disorders among school
teachers. BMC public health 14, 1211 (Nov 2014). https://doi.org/10.1186/1471-
2458-14-1211
23. Pront, L., Mller, A., Koschade, A., Hutton, A.: Gaming in Nursing Education:
A Literature Review. Nursing Education Perspectives 39(1), 23 (Feb 2018).
https://doi.org/10.1097/01.NEP.0000000000000251
24. Lu, A.S., Kharrazi, H.: A State-of-the-Art Systematic Content Analysis
of Games for Health. Games for Health Journal 7(1), 1–15 (Jan 2018).
https://doi.org/10.1089/g4h.2017.0095
25. Li, X., Yi, W., Chi, H.L., Wang, X., Chan, A.P.C.: A critical re-
view of virtual and augmented reality (VR/AR) applications in con-
struction safety. Automation in Construction 86, 150–162 (Feb 2018).
https://doi.org/10.1016/j.autcon.2017.11.003
26. Campbell, M., Fitzpatrick, R., Haines, A., Kinmonth, A.L., Sandercock, P., Spiegel-
halter, D., Tyrer, P.: Framework for design and evaluation of complex interventions
to improve health. BMJ : British Medical Journal 321(7262), 694–696 (Sep 2000)
27. Petit, A., Ha, C., Bodin, J., Rigouin, P., Descatha, A., Brunet, R., Goldberg, M.,
Roquelaure, Y.: Risk factors for carpal tunnel syndrome related to the work orga-
nization: a prospective surveillance study in a large working population. Applied
ergonomics 47, 1–10 (2015)
28. McDermott, H., Haslam, C., Clemes, S., Williams, C., Haslam, R.: Investigation of
manual handling training practices in organisations and beliefs regarding effective-
ness. International Journal of Industrial Ergonomics 42(2), 206–211 (Mar 2012).
https://doi.org/10.1016/j.ergon.2012.01.003
29. Clemes, S.A., Haslam, C.O., Haslam, R.A.: What constitutes effective manual han-
dling training? A systematic review. Occupational Medicine 60(2), 101–107 (Mar
2010). https://doi.org/10.1093/occmed/kqp127
30. Collado-Mateo, D., Merellano-Navarro, E., Olivares, P.R., Garc´ıa-Rubio, J., Gusi,
N.: Effect of exergames on musculoskeletal pain: A systematic review and meta-
analysis. Scandinavian Journal of Medicine & Science in Sports (April), 1–12
(2017). https://doi.org/10.1111/sms.12899
31. Rehabilitation-Oriented Serious Game Development and Evaluation Guidelines
for Musculoskeletal Disorders. JMIR Serious Games 5(3), e14 (jul 2017).
https://doi.org/10.2196/games.7284
32. Jansen-Kosterink, S.M., Huis in ’t Veld, R.M., Sch¨onauer, C., Kaufmann, H., Her-
mens, H.J., Vollenbroek-Hutten, M.M.: A Serious Exergame for Patients Suffering
from Chronic Musculoskeletal Back and Neck Pain: A Pilot Study. Games for
Health Journal 2(5), 299–307 (oct 2013). https://doi.org/10.1089/g4h.2013.0043
33. Deutsch, J.E.: Virtual reality and gaming systems to improve walking and
mobility for people with musculoskeletal and neuromuscular conditions. Stud-
17
ies in health technology and informatics 145(February 2009), 84–93 (2009).
https://doi.org/10.3233/978-1-60750-018-6-84
34. Daesign: Serious Game Daesign : ”Halte aux TMS” (FR) (2013), https://www.
youtube.com/watch?v=oLlgg18MycM,https://www.daesign.com/
35. Rodrigues, M.A.F., Macedo, D.V., Pontes, H.P., Serpa, Y.R., Serpa, Y.R.: A seri-
ous game to improve posture and spinal health while having fun. In: 2016 IEEE
International Conference on Serious Games and Applications for Health (SeGAH).
pp. 1–8. IEEE (may 2016). https://doi.org/10.1109/SeGAH.2016.7586260
36. Rodrigues, M.A.F., Serpa, Y.R., Macedo, D.V., Sousa, E.S.: A seri-
ous game to practice stretches and exercises for a correct and healthy
posture. Entertainment Computing (June 2016), 0–1 (nov 2017).
https://doi.org/10.1016/j.entcom.2017.11.002
37. Freitas, H., Soares, F., Carvalho, V., Matos, D.: Serious Games Develop-
ment as a Tool to Prevent Repetitive Strain Injuries in Hands: First Steps.
In: Teaching and Learning in a Digital World, vol. 1, pp. 954–964 (2018).
https://doi.org/10.1007/978-3-319-73210-7 108
38. Bartolome, N.A., Zorrilla, A.M., Zapirain, B.G.: Can game-based therapies
be trusted? Is game-based education effective? A systematic review of the
Serious Games for health and education. In: 2011 16th International Con-
ference on Computer Games (CGAMES). pp. 275–282. IEEE (jul 2011).
https://doi.org/10.1109/CGAMES.2011.6000353
39. Sportillo, D., Avveduto, G., Tecchia, F., Carrozzino, M.: Training in VR: A Pre-
liminary Study on Learning Assembly/Disassembly Sequences. Augmented and
Virtual Reality 9254 (2015). https://doi.org/10.1007/978-3-319-22888-4
40. Gobron, S., Wenk, N., Schmitt, C., Charrotton, Y., Fauquex, A., Lauria, M., De-
gache, F., Frischknecht, R.: Serious Games for Rehabilitation Using Head-Mounted
Display and Haptic Devices. In: Augmented and Virtual Reality, pp. 199–219
(2015). https://doi.org/10.1007/978-3-319-22888-4 15
41. Saenz-de Urturi, Z., Garcia-Zapirain Soto, B.: Kinect-Based Virtual Game for the
Elderly that Detects Incorrect Body Postures in Real Time. Sensors 16(5), 704
(may 2016). https://doi.org/10.3390/s16050704
42. Vignais, N., Bernard, F., Touvenot, G., Sagot, J.C.: Physical risk factors identifica-
tion based on body sensor network combined to videotaping. Applied Ergonomics
65, 410–417 (Nov 2017). https://doi.org/10.1016/j.apergo.2017.05.003
43. Zare, M., Malinge-Oudenot, A., Hglund, R., Biau, S., Roquelaure, Y.: Evaluation
of ergonomic physical risk factors in a truck manufacturing plant: case study in
SCANIA Production Angers. Industrial health 54(2), 163–176 (2016)
44. McAtamney, L., Nigel Corlett, E.: RULA: a survey method for the investigation of
work-related upper limb disorders. Applied Ergonomics 24(2), 91–99 (Apr 1993).
https://doi.org/10.1016/0003-6870(93)90080-S
45. Cicirelli, G., Attolico, C., Guaragnella, C., D’Orazio, T.: A Kinect-Based
Gesture Recognition Approach for a Natural Human Robot Interface. In-
ternational Journal of Advanced Robotic Systems 12(3), 22 (mar 2015).
https://doi.org/10.5772/59974
46. Pirovano, M., Ren, C.Y., Frosio, I., Lanzi, P.L., Prisacariu, V., Murray, D.W.,
Borghese, N.A.: Robust Silhouette Extraction from Kinect Data. pp. 642–651
(2013). https://doi.org/10.1007/978-3-642-41181-6 65, http://link.springer.
com/10.1007/978-3-642-41181- 6{_}65
47. N. A. Ansari, D.M.J.S.: Evaluation of work Posture by RULA and REBA: A Case
Study. IOSR Journal of Mechanical and Civil Engineering (IOSR-JMCE) 11 (July
2014)
18
48. Wenk, N., Gobron, S.: Reinforcing the difference between Simulation,, Gamifica-
tion, and Serious Game. Gamification & Serious Game pp. 1–3 (2017)
49. Sisto, M., Wenk, N., Ouerhani, N.: Augmented Reality, Virtual Reality, and Com-
puter Graphics 10325, 35–49 (2017). https://doi.org/10.1007/978-3-319-60928-7
