IERC 2012 Paper Formatting Guidelines - X

Proceedings of the 2014 Industrial and Systems Engineering Research Conference
Y. Guan and H. Liao, eds.
Ergonomic Assessment of Patient Under-arm Lifting Technique
Using Digital Human Modeling
Chenyu Ha, Wen Cao, and Mohammad T. Khasawneh
Department of Systems Science and Industrial Engineering
State University of New York at Binghamton
Binghamton, NY 13902
Abstract
Work-related musculoskeletal disorders (WMSDs) are becoming increasingly common in healthcare settings. The
occurrence of WMSDs in healthcare is supported by studies of the American Nurses Association and the American
Hospital Association. In particular, patient handling activities, such as lifting using a wide variety of techniques,
have been found to lead to various types of WMSDs among the nursing staff. Therefore, the primary objective of
this study is to simulate and evaluate patient Under-arm lifting tasks from an ergonomic perspective using digital
human modeling. In this experimental design-based study, clinical staff’s gender (CSG), clinical staff’s weight and
height (CSWH) were used as independent variables. The output of the comfort assessment (CA) and rapid upper
limb assessment (RULA) toolkits were used as dependent variables. The results showed that trunk thighs, knees and
foot calves are the most uncomfortable parts of the lifter during the patient lifting process. Moreover, according to
RULA, female clinical staff may experience less discomfort during Under-arm lifting tasks than male clinical staff.
The results of this research can assist hospital administrators in designing more effective patient lifting protocols.
Keywords
Patient Under-arm Lifting, Digital Human Modeling, Comfort Assessment, Rapid Upper Limb Analysis
1 Introduction
1.1 Overview
Work-related Musculoskeletal Disorders (WMSDs) are very common in the workplace [1]. They impact the health
of the working population in a wide variety of industries. WMSDs can lead to injuries to the soft tissues of human
bodies (muscles, tendons, ligaments, and nerves) and those that are severe can trigger career-ending injuries that
entail surgeries [2]. WMSDs, including back injury, are perhaps among the most expensive, but most preventable
workplace injuries in the United States [3]. The American Nurses Association (ANA) reports that physical injuries
occur in nursing staff at a rate which is twice that found in the general working population [4].
Low Back Disorders (LBDs) are among the most common work-related injuries. There are several factors that lead
to LBD while working, as shown in Table 1, with lifting/forceful movement and whole body vibration being the
strongest indicators [7]. Table 2 shows a report from the Bureau of Labor Statistics (BLS), which also referred to a
dataset for measuring healthcare related LBDs. Also, the report stated that the percentage of shoulder and back
accounted for total human parts in the easiness of getting injured is 12.6% and 56.2% respectively [8]. It is clear that
nursing staff and healthcare related personnel can easily acquire WMSDs in working area, thereby require an
assessment of manual patient lifting postures in healthcare settings. Winkelmolen et al. [6] introduced and evaluated
five lifting techniques, as shown in Figure 1: Australian lift, Orthodox lift, Barrow lift, Through-arm lift and Underarm lift. The Under-arm lifting technique was selected for evaluation in this study.
Ha, Cao and Khasawneh
Body Part Risk Factor
Back
Lifting/forceful movement
Awkward posture
Heavy physical work
Whole body vibration
Static work posture
Table 1 Different factors lead to LBDs [7]
Strong evidence
Evidence
Insufficient Evidence
Evidence of No Effect
√
√
√
√
√
Table 2 MSDs in nursing staff for various standard occupation classifications (SOCs) [8]
2000/2010 SOC
All Cases
Musculoskeletal Disorders
Number
Incident Rate
Number
Incidence Rate
Nursing Aides, Orderlies, & Attendants
47,840
443.9
25,780
239.2
(2000 SOC)
Nursing Assistants (2010 SOC)
46,520
-25,010
-Orderlies (2010 SOC)
1,310
-770
-Registered Nurses (2000 SOC)
27,950
135.7
12,000
58.2
Registered Nurses (2010 SOC)
27,610
-11,880
-Nurse Anesthetists 92010 SOC)
40
-20
-Nurse Midwives (2010 SOC)
----Nurse Practitioners (2010 SOC)
280
-90
--
Figure 1 Five lifting techniques (left to right: Australian lift, Through-arm lift, Under-arm lift, Orthodox lift and
Barrow lift) [6]
1.2 Digital Human Modeling (DHM)
DHM is an appealing technology that is widely used in today’s modern industry. DHM uses biomechanically
accurate human figures (of various genders and anthropometric characteristics) to simulate basic and complex
human performance in a wide variety of settings. Even though DHM has been very successful in the manufacturing
and automotive industry, it is particularly important in healthcare [9]. Healthcare related tasks including complex
postures and activities that are difficult to simulate in real life experiments. Also, due to shortage of healthcare
professionals, conducting studies in healthcare settings with human subjects poses significant challenges. Therefore,
DHM provides a platform for investigating, evaluating and performing working scenarios in all industries and
healthcare is no exception [5].
2 Literature Review
Colombini and Occhipinti [1] reported that upper limb work-related musculoskeletal disorders (UL-WMSDs)
represent the most frequent pattern of work injuries in industry and manufacturing. This article mainly provides
standards for how to compare and combine traditional and advanced ergonomic assessment methods to relieve those
who suffer from UL-WMSDs in the general working population. Charney et al. [10] stated that compensable back
injuries were commonly found in healthcare settings in Washington State. The main reason was found to be the
frequent lifting and handling activities. The authors suggested using some methods or devices to replace manual
handling activities. The results showed a 43% decrease of patient-handling injury claims from 2000 to 2004 and a
50% decrease of patient handling/lifting activities time lost [10].
According to a study by Marras et al. [7], LBD is reported as a frequent musculoskeletal problem in the workplace,
which can be controlled using ergonomic interventions. The authors also examined LBD related human body parts,
which can be used to find factors associated with LBD. Hignett [11] reported that the Ovako working posture
Ha, Cao and Khasawneh
analysis system (OWAS) had been used in evaluating back pain in the nursing profession. Standards of occupational
hazard represent back pain in Hignett’s research. Hignett [11] used OWAS to measure the routine activities
performed by nurses in elder care programs. A grouping data analysis method was adopted and resulted in a higher
percentage of harm during patient handling activities than during non-patient related ones. The author suggested that
attitudinal modifications should be applied into manual lifting activities in healthcare to reduce back pain frequency.
Michalak’s [12] study provided suggestions on improving working processes with ergonomic interventions due to
the increase in the frequency of WMSDs in healthcare. The leading factors contained repetitive motion, pinch-grasp
force, vibration, and prolonged awkward positions, especially in private dental clinics. Mchugh [13] reported that
cumulative trauma disorders (CTDs) are the most prevalent injuries incurred by repetitive activities and posture due
to an inappropriate workstation design.
2.2 The Risks of Patient Handling/Lifting
According to the study of Freitag et al. [14], stress to the spinal column has been analyzed. Many factors, such as
awkward body postures, handling of loads, etc., lead to the stress of the spinal column. Using computer-assisted
measurement system, video recorded body postures assessed on the basis of various criteria. The results showed that
many stressful tasks were performed in a routine shift of the nursing profession. Evanoff et al. [15] reported that
patient transfer and handling tasks were one of the important factors that lead to frequent clinical staff work-related
injuries. Time loss and the effectiveness and efficiency of mechanical lifts were evaluated in Evanoff et al.’s [15]
research in long-term care (LTC) facilities. In the pre-intervention and post-intervention period, a large proportion of
reduction in time loss and number of work-related injuries claims was seen in the LTC facilities. As concluded by
the authors, mechanical lifting devices should be widely used in LTC and acute care facilities.
Similarly, from a study by Engst et al. [16], patient and/or resident handling has been identified as a cause of
healthcare work-related injuries. Mitigating the risk of such tasks can be done effectively by ceiling-based lifting
devices. An evaluation plan was implemented to assess the effectiveness of ceiling lifting devices compared with
manual lifting activities. Overhead ceiling lifting devices were preferred among the nursing population after
conducting a questionnaire. As concluded in further research from Engst et al. [16], a significant reduction in
WMSDs was seen in nursing staff that utilized overhead lifting devices. Compared with manual lifting based tasks,
overhead or other mechanical lifting techniques cost less due to the reduction of work-related injuries.
Motacki et al. [3] reported that safe patient handling and movement (SPHM) was applied into various healthcare
facilities for mitigating the risk of WMSDs. The SPHM program implementation could effectively influence the
performance of nursing staff and related workers. Wrigley et al. [17] stated that no recent studies could discriminate
differences in the manual handling activities between healthy workers who obtain low back pain and those who do
not. The authors developed an analysis to identify how to distinguish the lifting techniques that would eventually
develop low back pain. A rating for the tested lifting techniques and an analysis of the variance was used to
determine the final results. Related waveform patterns, which would influence low back pain, were also found.
2.3 DHM Applications
Demirel et al. [18] discussed the benefits of DHM and researched its application in industry. CATIA V5 PLM
solution package and UGS Tecnomatix Jack DHM software were mentioned as strategies that can address industrial
challenges by improving the process of product development and design. Another study, also from Demirel et al.
[18], emphasized DHM implementation in Product Lifecycle Management (PLM). As stated, a Formula 1 race car
and marine vessel were designed by the application of PLM and DHM software packages. The results showed that
DHM tools were effective in the product design and development process, which illustrated how DHM software can
deal with complex systems and human-machine interaction devices. Similarly, Chang et al. [19] tested the potential
of DHM software packages to assess a real life environment to reduce WMSDs with a focus on workplace layout.
The data was captured in a dynamic simulation and ergonomics evaluation to visualize and enhance product
development in the virtual scenarios.
Möbus et al. [20] provided a study of the Human Centered Design (HCD) of the Partial Autonomous Driver
Assistance Systems (PADAS) for simulating traffic scenarios. The result showed that the real-time control can be
effectively implemented by using DHM software packages. Hanson et al. [21] aimed to provide a guide to determine
the effectiveness and efficiency of such simulation tools in car’s interior design in virtual environments. The results
showed that the guide was proven to be useful from users’ feedbacks, and provided contents that helped users to
select DHM tools and reduce repetitive work. From another perspective, Carruth et al. [22] stated that the
Ha, Cao and Khasawneh
computational cognitive models had been applied to prove the effectiveness and accuracy of cognition in a simple
human-machine interaction task. The results showed the extent of DHM’s ability to simulate cognitive tasks in the
virtual environment.
3. Methodology
3.1 Digital Human Modeling Software
Currently, automatic devices and equipment occupy a large proportion of modern industry [9]. However, manual
activities are still required for delivering services, production stages, and routine activities. In this case, human
factors or ergonomics play a significant role in enhancing and guaranteeing the success of certain operations [23].
Unlike traditional ergonomic assessment methods, software such as the Jack software provides a more
comprehensive approach to ergonomic assessment. Jack allows users to create any kind of working environment.
The human figures Jack provides are anthropometrically and biomechanically accurate. Also, Jack provides
advanced scaling systems to measure human objects with accurate experiment results [9]. The cost of employing
personnel for testing quality and feasibility can be more than expected due to a large range of potential users that are
required [23]. Therefore, the Jack software has been utilized to conduct the DHM-based ergonomic assessment
study proposed in this research. Employing the Jack software or other similar tools provides the ability to test a wide
variety of scenarios that cannot be tested in a laboratory environment.
3.2 Under-arm Lifting Technique
This part of study aims to evaluate the effectiveness and efficiency of the Under-arm lifting technique. The Underarm lifting technique entails two clinical staff and one patient. Two clinical staff stand symmetrically and face each
other to coordinate. One of the clinical staff’s hands is put under the patient’s thigh with a carrying function.
Meanwhile, their other hand is placed under the patient’s axilla. Meanwhile, the patient relaxes and crosses their
arms against each other for coordinating with the clinical staff’s postures. By using the Under-arm lifting technique
in a work environment, the low back compression values are be obtained for analyzing the level of back pain.
Meanwhile, this study applied DHM instead of conducting real person into experiment.
3.3 Variables
Before the model and experiment were implemented, several preparations were completed. This study has four steps
for implementing the Under-arm lifting technique. First, independent and dependent variables were setup for
measuring different scenarios. The independent variables include Clinical Staff Gender (CSG (M, F)), Patient
Weight and Height (PWH (5%, 50%, 95%)), and Clinical Staff Weight and Height (CSWH (5%, 50%, 95%)). PWH
and CSWH are defined by the ANSUR database from the Jack software. The dependent variables include Comfort
Assessment (CA) and Rapid Upper Limb Assessment. The total number of combinations tested is 18 (Table 3).
Different measurements of the human body are shown in Figure 2, demonstrating different variables containing
CSWH and CSG as well as presenting nurses' postures while lifting patients.
CSG
M
M
M
M
M
M
M
M
M
CSWH
5%
5%
5%
50%
50%
50%
95%
95%
95%
Table 3 Combination of the independent variables
PWH
CSG
CSWH
5%
F
5%
50%
F
5%
95%
F
5%
5%
F
50%
50%
F
50%
95%
F
50%
5%
F
95%
50%
F
95%
95%
F
95%
PWH
5%
50%
95%
5%
50%
95%
5%
50%
95%
Ha, Cao and Khasawneh
Figure 2 Variables in virtual environment
3.4 Environment Setting
The second step is to simulate the Under-arm lifting posture in a virtual environment by using Jack 7.1. The
experiment setup can be summarized into the following steps: 1) Human models were created from the library
software from ANSUR measurements database (the human property tool is used for setting human positions,
constraints, and appearance); 2) The equipment (e.g., operation bed and television) is downloaded from Google 3D
warehouse and imported into Jack software; 3) Human models' postures and positions were adjusted by using human
body control tools installed in Jack 7.1 for preparing animation and analysis (the human control tool is for adjusting
human postures in several human parts, including hands, arm, shoulder, etc.; weights and loads were added onto
human models' hands with the axis of palm center on both hands); 4) An animation of different phases for two
clinical staff transferring one patient from a bed to another using Under-arm lifting technique was created from the
software, of which the sequence was then coordinated to adjust the accuracy and comprehensiveness of this
experiment. While finishing the experiment animation, the data of CA and RULA was collected from the toolkits of
Jack 7.1. Figure 3 shows a comprehensive view of the environment.
3.5 Comfort Assessment (CA)
The Comfort Assessment toolkit (which is based upon Porter’s 1998 database) provides ratings on the comfort level
of several important parts of the human body. Additionally, it provides the upper and lower bounds for comfort
ratings in addition to the mode values, which are the most comfortable values for each particular human part. Figure
4 shows the CA data collection interface during animation. However, the characteristics of those values are difficult
to identify and large differences exist between their upper and lower bounds. Therefore, it is difficult to locate them
in the total comfort range and show the degree of comfort for various human parts. Therefore, equation (1) was
setup to describe how to transform and standardize the raw values to more readable values. Those transformed
values were recorded in the animation module and the highest values were chosen as the final ones. The highest
values represent the degree of harm that a specific action causes, and in effect provides more accurate suggestions
on patient lifting/handling activities.
 MD  OV /( HV  MD), when OV  MD

Transformed Value   MD  OV /( MD  LV ), when OV  MD
0, Otherwise

(1)
where MD is mode value, OV is the original value, HV is high value, and LV is low value. The most comfortable
value is 0 while any number between 0 and1 is acceptable.
Ha, Cao and Khasawneh
Figure 3 Comprehensive view of experiment setting
3.6 Rapid Upper Limb Analysis (RULA)
The RULA toolkit is one of the task analysis toolkit packages in Jack. Several task entry modules are provided in
this toolkit for specific analysis. The postures of human figures are adjusted manually by users. However, in spite of
the original postures, there exist muscle use forces and load options in both parts of the task entry (arm, wrist and
neck, trunk respectively). In both parts, there are three options for muscle use: 1) normal, no extreme use, 2) mainly
static (e.g., held for longer than 1 minute), and 3) action repeated more than 4 times per minute. Similarly, there are
four options in forces and loads: 1) <2 kg intermittent load; 2) 2-10 kg intermittent load; 3) 2-10 kg static load or 210 kg repeated load, and 4) more than 10 kg static (shock forces). Additionally, the legs and feet section is added.
There are also three options within the legs and feet section: 1) seated, legs and feet well supported (weight even), 2)
standing, weight even (room for weight changes, and 3) legs/feet not supported (weight distribution uneven). In this
study, normal muscle use, more than 10 kg static (shock force), standing, weight even (room for weight changes)
were selected to output the RULA score. Figure 4 shows the RULA interface during animation.
Figure 4 CA and RULA data collection during animation
4 Results and Discussion
4.1 Comfort Assessment (CA)
Table 4 shows the transformed results from the CA toolkit. The results were calculated in the largest rotation angle
of the human figure’s back. Several body parts experienced discomfort during the lifting process. In the transformed
value results, the lowest value represents the most comfortable part of the human subjects. The comfort zone is
between 0 and 1. Values above 1 should be considered as discomfort to the subject. From the results, the smallest
value is 0.14 (right upper arm flexion of 95% female) and the second smallest value, which is the most comfort body
part, is both the right and left upper arm flexion value of 50% female clinical staff perform Under-arm lift. However,
Ha, Cao and Khasawneh
the largest value is 4.50 (Right trunk thigh of 5% male). Trunk thigh, knee, and foot calf are most uncomfortable
areas in both female and male clinical staff. Meanwhile, the left elbow also experienced some discomfort in male
clinical staff. To be more specific, 95% of males may experience discomfort in the left upper arm and 5% female
clinical staff may experience discomfort in the right elbow. However, comfort ratings in the male’s right upper arm
and the female’s right elbow did not exceed 1. Generally, 5% clinical staff experienced higher values than the
others, except the upper arms of both male and female clinical staff.
CSWH
Head Flexion
Upper Arm Flexion Right
Upper Arm Flexion Left
Elbow Included Right
Elbow Included Left
Trunk Thigh Right
Trunk Thigh Left
Knee Included Right
Knee Included Left
Foot Calf Included Right
Foot Calf Included Left
Table 4 Comfort assessment results
Male
5%
50%
95%
0.69
0.69
0.69
0.33
0.81
0.94
0.39
0.92
1.04
0.57
0.44
0.44
1.96
1.92
1.89
4.50
3.17
3.21
3.22
3.10
3.22
3.09
3.09
3.09
2.99
2.99
2.99
1.88
1.88
1.88
1.78
1.78
1.78
5%
0.49
0.68
0.62
1.22
0.76
3.35
3.34
2.56
2.53
1.96
1.84
Female
50%
0.49
0.23
0.23
0.90
0.69
3.15
3.14
2.56
2.53
1.96
1.84
95%
0.49
0.14
0.29
0.77
0.53
2.80
2.79
2.56
2.53
1.96
1.84
4.2 Rapid Upper Limbs Analysis (RULA)
The RULA toolkit provided two groups of ratings. The basic principle of the score rating is that larger scores
indicate that it was more difficult for the human to accomplish the task at hand. Group A contains upper arm, lower
arm, wrist, and wrist twist while Group B contains neck and trunk. Figure 5 shows that throughout the entire Group
A males and females experienced the same scores, but their total score in the group differed (Female = 8, Male = 9).
From the perspective of the body parts, the Under-arm lifting technique was less painful for female clinical staff
than male clinical staff. Figure 5 also shows the Group B score of both female and male clinical staff. Females
experienced much lower scores on both neck (Male = 4, Female = 1) and trunk (Male = 4, Female = 3) and on the
overall rating (Male = 10, Female = 6). It is clear that it is easier for females to perform Under-arm lifting technique
with regard to the comfort level of every body part.
10
9
8
7
6
5
4
3
2
1
0
10
9
8
7
6
5
4
3
2
1
0
Female
Male
Female
RULA Score
RULA Score
Male
Upper Arm
Lower
Wrist
Wrist
Arm Human Parts Twist
Total
Neck
Trunk
Total
Human Parts
Figure 5 Groups A (left) and B (right) human parts RULA score
5 Conclusions and Future Work
5.1 Conclusions
This study was conducted with the aim of evaluating the Under-arm lifting techniques performed by clinical staff so
as to reduce the occurrence of LBDs. The aforementioned WMSDs, including LBDs, have become a frequent
problem for clinical staff in healthcare areas. Members of clinical staff are particularly impacted by LBDs due to the
awkward postures and repetitive motions associated with their patient lifting and handling activities. The lower back
pain and musculoskeletal injuries caused by patient lifting have not received sufficient attention and, therefore, an
Ha, Cao and Khasawneh
efficient method of its alleviation has not been sought. By scrutinizing the comfort ratings in different parts of the
digital caregivers, DHM can not only illustrate clearly what parts of the human body should receive more protection,
but also assist in finding alternative ways to avoid injuries.
This study applies DHM to simulate and evaluate the Under-arm lifting technique based on CA values and RULA
scores. From the CA values, it can be determined that trunk thighs, knees, and foot calf are the most uncomfortable
areas. Furthermore, several other body parts may experience discomfort in specific CSWH. The RULA system
revealed that female clinical staff experienced better scores than male clinical staff. From the above results and
discussion, it can be concluded that female clinical staff can perform better with manual patient lifting tasks, but
male clinical staff can more easily acquire back injuries than female clinical staff. It can also be concluded that this
kind of manual lift technique will still bring serious back injury to the lifters. Considering that several previous
studies reported that other postures or lifting techniques were as bad as the Under-arm lifting technique [9, 11], it is
clear that all manual lifting postures for comparatively heavy patients are potentially dangerous.
Mechanical lifting device (MLD) is a new method of lifting that can potentially address the difficulties of patient
handling and lifting techniques. MLDs can be subcategorized into floor-based lifting devices and overhead lifting
devices. MLDs can assist clinical staff to accomplish certain tasks without the risk of obtaining WMSDs through
repeated patient handling/lifting. However, there are still limitations to using MLDs. Tight budgets and its inability
to handle urgent patient handling/lifting are two factors that can prevent MLDs from being implemented. In their
current conditions, healthcare facilities simply cannot use MLDs to replace manual lifting techniques.
5.2 Future Work
Based on the results of this study, there are still several experiments that could be explored in the future. For
example, it is possible to add several other independent variables into this study, such as patients' gender or clinical
staff postures. From the perspective of clinical staff’s posture, it is possible to setup different flexion angles for
clinical staff’s back and knees. In addition, wrist twist angle, neck flexion angle can also be added as variables for
identifying the best posture for manual lifting. In this study, independent variables have not been setup separately
between weight and height. Future studies could be more comprehensive if weight and height are setup separately.
Also, other independent variables can be chosen as patient’s age, patients’ height, patient’s weight and patient’s
gender. Moreover, Winkelmolen et al. [6] studied five separate lifting techniques that could be evaluated. It is
necessary to compare all of them to optimize the various patient lifting techniques further. Finally, MLDs can be
evaluated using DHM because patient comfort is an important criterion for evaluating MLDs in healthcare facilities.
Additionally, it is important to estimate the current proportion of MLD usage and manual patient lifting. Therefore,
data collection from several healthcare facilities is needed. Research in investigating clinical staff’s behavior during
patient lifting should also be taken into account for further studies.
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