Digital Human Modeling for Musculoskeletal Disorder Ergonomics Researches
in Healthcare
Le Zhang, Jian-wei Niu, Xiao-lin Feng, Si-yang Xu, Xin Li, Si-si Guo
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China
(hunterctzl@gmail.com)
Abstract - Musculoskeletal Disorder (MSD) is always
considered as one of the most significant occupational injury
in industry. In recent times, more and more people analyze
MSD in healthcare. Meanwhile, Digital Human Modeling
(DHM) can analyze human factors in Virtual Environment
(VE) by simulating industrial tasks. However, few people
involve DHM in healthcare MSD researches. This paper
presented an approach of applying DHM ergonomics
analysis in nursing investigation. The fundamentals of
Nursing Tasks (FNT) were simulated in Siemens Classic Jack
(Jack) with biomechanical DHM. Jack Tasks Analysis
Toolkits (TATs) were adopted to evaluate human factors in
MSD analysis. The TAT results and traditional questionnaire
investigation showed similar MSD regions. It indicates that
DHM has potentials to offer a visual analysis and enhance
approach for simulation of dynamic system in healthcare
MSD analysis to reduce the incidence of MSD in nursing.
Key Words - Digital Human Modeling Simulation,
Healthcare, Jack, Musculoskeletal Disorder, Nursing
I.
INTRODUCTION
Musculoskeletal Disorder (MSD) represents one of
the leading causes of occupational injury and disability in
industrial applications. For example, MSD researches
have been successfully launched in automobile assembly
plant[1]. Meanwhile ergonomists start turning to conduct
MSD researches in healthcare. MSD researches about
nursing healthcare tasks have been developed in the
United States, Europe, Japan and China[2-5]. Over the past
40 years, nursing has been steadily increasing the
publication output in MSD and in the last 10 years there
has been a substantial increase in all publications [6].
Most nursing MSD researches were based on
questionnaire investigation. The success of Nurse Early
Exit Study, Copenhagen Psychosocial Questionnaire and
Nordic Musculoskeletal Questionnaire proved the validity
that the adapted questionnaire had an acceptable structure
and provided reliable information from the nursing
profession in physical and psychosocial[7]. Self-report
survey for musculoskeletal symptoms, Psychometric
Evaluation Questionnaire
and
Job
Description
Questionnaire were used to identify the MSD among
perioperative nurses and technicians and demonstrated a
high prevalence of work-related MSD among nurses, with
84% pain complaint on lower back pain, 74% on ankle
and 74% on shoulder, and found 31% lower back pain,
followed by 24% ankle/knee pain to be the main causes of
absenteeism from work[8].
Digital Human Modeling (DHM) had been widely
used in New Product Launches[9], Product Lifecycle
Management[10] and Manufacturing Process[11]. DHM
tools have potentials to improve the product development
challenges and provide control of the entire process of
product design. Using DHM to create dynamic simulation
of healthcare work is an innovation in MSD researches
and fills the blank of biomechanical analysis in traditional
approaches. Motion structure representation algorithm in
DHM identifies the basic spatial–temporal structure of
human motion, and it can be generalized to produce an
infinite number of similar motion variants and create
basic motion simulation models[12]. Various nationality,
gender, accommodation percentage and size DHMs can
be generated in virtual environment (VE) to properly
accommodate the ergonomics designated and evaluation
of products and workplaces of the target population[13].
Application of DHM in healthcare MSD research can
provide visual result and improve the environment design
of healthcare.
This study proposed a nursing MSD research in
Chinese hospital. Siemens Classic Jack 7.1[14] (Jack) was
used as the DHM software to simulate nurses’ healthcare
work in VE. Jack has biomechanical DHM to simulate
dynamics works and Task Analysis Toolkits (TATs) to
investigate ergonomics in diverse environments. Totally
189 questionnaires were filled by nurses in a Chinese
hospital and 30 anthropometry statistics was collected to
create specific Chinese nurse DHM. Static simulation and
dynamic simulation were both analyzed by TATs and the
result was compared with the questionnaire investigation.
The organization of this paper is as follows. Section 2
introduces the processes of the research. Section 3 reports
the result of the analysis. Discussions are given in
Section4. Finally, Section 5 summarizes this study.
II.
METHOD
Firstly, questionnaires were used for traditional MSD
investigation. Five body MSD regions of Nordic Standard
Questionnaire were settled in the questionnaires. Then
DHM was used to simulate the nursing tasks. A VE of
Chinese hospital ward was built in Jack. To utilize Jack for
visual ergonomics analysis, static and dynamic
simulations of healthcare work were created and two
TATs were manipulated for investigation. The
methodology of healthcare MSD analysis was explained
in details. Comparison between the two approaches,
namely questionnaire investigation and DHM, was
addressed.
A.
Traditional MSD Questionnaire Investigation
One hundred and eighty-nine registered Chinese
female nurses from intensive care units, emergency room,
operation room etc. were asked to fill in anonymous
questionnaires. The questionnaire included three sections.
The first section collected anthropology information like
age, status, and weight. The next section included
questions for risk factor such as task type, task repetitive
times, and career duration. The last section provided MSD
symptoms of Nordic Standard Questionnaire.
Valid statistics was tested in SPSS 16.0[15] by
Chi-square. The subjects were divided into two groups,
manipulating tasks less than 10 times a day and more than
10 times a day. The initial assumption was no difference
between each group. With 95% Confidence Interval, we
refused the initial assumption when the P value is smaller
than 0.05 and considered there is significant difference
between each group. Odd Ratio proved the risk factors of
each region.
B.
Creation of Virtual Environment and DHM
The head nurses in each ward indicated that the most
common task of nursing was Fundamental of Nursing Task
(FNT). Thus, the simulation task was arranged as turning
patient’s body on bed, a FNT. Three experienced nurses
performed the entire process of turning a patient in bed.
Details of the task were recorded by video.
Before manipulating DHM to simulate FNT, a
hospital ward and Chinese nurse feature DHM must be
created in VE. Jack provides a function to input CAD
formats model in VE. A valid hospital bed CAD model
was created in Pro-E software and then input into Jack as
a figure. Based on the Chinese national anthropometric
standard (GB 10000-88, 1989), an anthropometric
database representing Chinese female adults aged 18-55
was created in Jack. The basic nurse figure was a 50th
percentile default Chinese female. To refine the figure, we
measured 10 major anthropometric measurements of 30
Chinese nurses and took the average to scale figure by
Advance Scaling function. The color of figure’s clothing,
shoes and hair were simulated according to the real
subjects.
The hospital bed, nurse and patient in VE, as Fig. 1
shows, simulated the nursing healthcare circumstance in
Jack.
Fig. 1 Virtual Environment of hospital bed and nurse in Jack
C.
DHM Simulation and Ergonomics Analysis
In Jack, the FNT process was divided into 8 postures
and 7 movements. The postures included start pose,
grasping distant arm, moving distant arm to the chest,
grasping legs, moving legs, grasping close arm, moving
close arm to the chest, and turning body. Those
movements were created by Animation Tool to present the
animation between two postures and combined together
as a fluent movie. Fig. 2 shows each motion.
Lower Back Analysis Tool (LBAT) and Static
Strength Prediction (SSP) provided dynamic and static
analysis in Jack. LBAT evaluated the spinal forces acting
on virtual nurse’s lower back under every motion and
loading condition, flagged the exact moments when the
compression forces exceed NIOSH limits. SSP calculated
the percentage of a working population that has the
strength to perform the task based on posture, exertion
requirements and anthropometry. The results were
presented by body regions. The region is considered
dangerous when the percentile is lower than 75%.
III. RESULTS
Chi-square analysis was presented in TABLE I.
When doing FNT more than 10 times a day, shoulder
(P=0.056, OR=2.062), torso (P=0.015, OR=2.543) and
knee (P=0.001, OR=3.151) faced significant risk to have
MSD symptoms. In contrast, elbow and ankle suffered
less MSD in FNT.
LBAT indicated that the load of lower back was over
the NOISH recommendation when DHM bended torso to
lift patient’s body. Fig. 3 shows the largest force on back
when nurse turning patient’s body. TABLE II lists the
results of SSP. Shoulder, torso and knee were less than
75% in most lifts. Ankle was between 75% and 90% in
some lifts while elbow was higher than 90%.
Fig. 2 Eight postures represent motions in dynamic nursing healthcare simulation
a: start pose; b: grasping distant arm; c: moving distant arm to the chest; d: grasping legs; e: moving legs;
f: grasping close arm; g: moving close arm to the chest; h: and turning body
simulation were operated to present risk factors in nursing
MSD analysis. The questionnaires showed which region
had significant relation to risk factors. In Jack, TATs gave
force on lower back and MSD risks of each region. In
addition, SSP and questionnaire investigation had a
similar result indicating that shoulder and torso suffer the
most significant MSD in FNT, elbow and ankle don’t
have significant risk in FNT.
In future work, more studies should be provided to
prove the validity of statistics analysis utility in DHM
ergonomics research. Questionnaire investigation should
be used to collect nurses’ anthropometry and
psychological information and improve the reliability of
DHM. With the improvement of DHM, virtual reality can
be added in nursing training. Nurse with digital devices
can perform healthcare work in VE and represented by
DHM. Thus, MSD can be improved by virtual interactive
design of nursing processes[16].
V.
Fig. 3 Result of Lower Back Analysis
TABLE I Chi-Square test result of fundamental of nursing task
Body Region
Odd Ratio
P value
CI（95%）
Shoulder
2.062
0.056
0.943-4.508
Elbow
1.686
0.113
0.801-3.549
Torso
2.543
0.015
1.187-4.451
Knee
3.151
0.001
1.55-6.405
Ankle
1.701
0.091
0.848-3.415
Reference group does FNT less than 10 times a day, while test group
does more than 10 times a day.
IV. DISCUSSION
In this study, questionnaire investigation and DHM
CONCLUSION
Considering MSD causes numerous occupational
injuries in healthcare, people should pay more attention
on nursing MSD investigation. This study investigated the
Chinese nurses’ MSD by questionnaires and DHM. The
result indicated that traditional questionnaire investigation
and DHM analysis in healthcare MSD research matched
well. In detail, two approaches both pointed out that
shoulder and torso had significant MSD in PNT.
Questionnaires provided the important information to
DHM and statistics analysis. DHM showed visual result
by dynamic simulation of FNT in the VE. With DHM
simulation, Jack analyzed MSD by TATs and showed
force and risk percentile to indicate risk factors. More
DHM simulation and ergonomics analysis should be
applied in the future and DHM simulation should be used
to train nursing work in virtual reality to prevent the MSD
and improve nursing process.
TABLE II Risk percentile of each body region in 4 motions
Risk Percentile
Moving Distant Hand
Moving Legs
Moving Close Hand
Turning Body
less than 75
Knee, Shoulder, Torso
Shoulder, Torso
Knee, Shoulder
Knee, Shoulder, Torso
75 to 90
Elbow
Ankle, Knee
Ankle, Torso
Ankle, Elbow
90 to 100
Ankle
Elbow
Elbow
Smaller risk percentile means the body region is easier to get musculoskeletal disorder.
ACKNOWLEDGEMENT
The study is supported by the Student Research
Training Program of University of science and technology
Beijing (No. 11040223).
[12]
[13]
REFERENCE
K. Fredriksson, C. Bildt, G. Hagg, A. Kilbom, “The impact
on musculoskeletal disorders of changing physical and
psychosocial work environment conditions in the
automobile industry”, International Journal of Industrial
Ergonomics, vol. 28 pp. 31–45, 2001.
[2] N. N. Menzel, S. M. Brooks, T. E. Bernard, A. Nelson,
“The physical workload of nursing personnel: association
with musculoskeletal discomfort”, International Journal of
Nursing Studies, vol. 41, pp. 859–867, 2004.
[3] M. Simon, P. Tackenberg, A. Nienhaus, M. Estryn-Behar,
P. M. Conway, H. M. Hasselhorn, “Back or
neck-pain-related disability of nursing staff in hospitals,
nursing homes and home care in seven countries- results
from the European NEXT-Study”, International Journal of
Nursing Studies, vol. 45, pp. 24–34, 2008.
[4] D. R. Smith, M. Mihashi, Y. Adachi, H. Koga, T. Ishitake,
“A detailed analysis of musculoskeletal disorder risk
factors among Japanese nurses”, Journal of Safety
Research, vol. 37, pp. 195 – 200, 2006.
[5] S. S. Yeung, A. Genaidy, J. Deddens, S. Sauter, “The
relationship between protective and risk characteristics of
acting and experienced workload, and musculoskeletal
disorder cases among nurses”, Journal of Safety Research,
vol. 36, pp. 85– 95, 2005.
[6] C. Hale, J. Hill, “Locating the evidence base for
musculoskeletal nursing: An overview of the
rheumatology nursing literature”, International Journal of
Nursing Studies, vol. 43, pp. 507–518, 2006.
[7] N. Arsalani, M. Fallahi-khoshknab, M. Ghaffari, M.
Josephson, M. Lagerstrom, “Adaptation of questionnaire
measuring working conditions and health problems among
Iranian nursing personnel”, Asian Nursing Research, vol. 5,
pp. 177-182, 2011.
[8] A. Sheikhzadeh, C. Gore, J. D. Zuckerman, M. Nordin,
“Perioperating nurses and technicians’ perceptions of
ergonomic risk factors in the surgical environment”,
Applied Ergonomics, vol. 40, pp. 833–839, 2009.
[9] H. O. Demirel, V. G. Duffy, “Applications of digital
human modeling in industry”, Springer-Verlag Berlin
Heidelberg, HCII 2007, pp. 824–832, 2007.
[10] H. O. Demirel, V. G. Duffy, “Digital human modeling for
product lifecycle management”, Springer-Verlag Berlin
Heidelberg, HCII 2007, pp. 372–381, 2007.
[11] J. Santos, J. M. Sarriegi, N. Serrano, J. M. Torres, “Using
ergonomic software in non-repetitive manufacturing
[1]
[14]
[15]
[16]
processes: A case study”, International Journal of
Industrial Ergonomics, vol. 37, pp. 267–275, 2007.
Woojin Park, Don B. Chaffin, Bernard J. Martin, Julian J.
Faraway, “A computer algorithm for representing
spatial–temporal structure of human motion and a motion
generalization method”, Journal of Biomechanics, vol. 38,
pp. 2321–2329, 2005.
K. Jung, O. Kwon, H. You, “Development of a digital
human model generation method for ergonomic design in
virtual environment”, International Journal of Industrial
Ergonomics, vol. 39, pp. 744–748, 2009.
Siemens PLM Software Inc, Simens Classic Jack:
http://www.plm.automation.siemens.com/zh_cn/products/t
ecnomatix/assembly_planning/jack/classic_jack.shtml,
retrieval at May 2, 2012.
IBM, IBM SPSS software: http://www-01.ibm.com/softwa
re/analytics/spss/, retrieval at May 2, 2012
R. Tian, V. G. Duffy, “Computerized task risk assessment
using digital human modeling based Job Risk
Classification Model”,
Computers & Industrial
Engineering, vol. 61, pp. 1044–1052, 2011.