1 s2.0-s2093791114000250-main

Accepted Manuscript
Development of decision support system for analysis and solutions of prolonged
standing at workplace
Isa Halim, PhD Hambali Arep, PhD Seri Rahayu kamat, PhD Rohana Abdullah, MSc
Abdul Rahman Omar, PhD Ahmad Rasdan Ismail, PhD
PII: S2093-7911(14)00026-2
DOI: 10.1016/j.shaw.2014.04.002
Reference: SHAW 41
To appear in: Safety and Health at Work
Received Date: 30 December 2013
Accepted Date: 15 April 2014
Please cite this article as: Halim I, Arep H, kamat SR, Abdullah R, Rahman Omar A, Ismail AR,
Development of decision support system for analysis and solutions of prolonged standing at workplace,
Safety and Health at Work (2014), doi: 10.1016/j.shaw.2014.04.002.
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Development of decision support system for analysis and solutions of prolonged standing1
at workplace2
3
Isa HALIM, PhD1
, Hambali AREP, PhD1
, Seri Rahayu KAMAT, PhD1
, Rohana4
ABDULLAH, MSc2
, Abdul Rahman OMAR, PhD3
, Ahmad Rasdan ISMAIL, PhD4
5
1
Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian6
Tunggal, Hang Tuah Jaya, Melaka, Malaysia.7
2
Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, 76100 Durian8
Tunggal, Hang Tuah Jaya, Melaka, Malaysia.9
3
Faculty of Mechanical Engineering, Universiti Teknologi MARA, 40450 Shah Alam,10
Selangor, Malaysia.11
4
Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang,12
Malaysia.13
14
15
Correspondence to:16
Isa Halim17
Faculty of Manufacturing Engineering18
Universiti Teknikal Malaysia Melaka19
76100 Durian Tunggal, Hang Tuah Jaya, Melaka, Malaysia.20
Tel.: +606-331650121
Fax: +606-331641122
e-mail: isa@utem.edu.my23
24
Running tittle: Decision support system for prolonged standing25

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TITLE: Development of decision support system for analysis and solutions of prolonged1
standing at workplace2
ABSTRACT3
Background: Prolonged standing has been hypothesized as one of vital contributors to4
discomfort and muscle fatigue to workers at workplace.5
Objectives: The objective of this study is to develop a decision support system that can6
provide systematic analysis and solutions in minimizing discomfort and muscle fatigue7
associated with prolonged standing.8
Methods: Integration of object oriented programming (OOP) and Model Oriented9
Simultaneous Engineering System (MOSES) were deployed to design the architecture of10
the decision support system.11
Results: Validation of the decision support system was carried out in two manufacturing12
companies. The validation process demonstrated that the decision support system has13
generated reliable results.14
Conclusion: Therefore, this study concluded that the decision support system is a reliable15
advisory tool for analysis and solutions related to discomfort and muscle fatigue16
associated with prolonged standing. The decision support system is suggested to undergo17
further testing for commercialization purpose.18
19
Keywords: Decision support system, Discomfort, Ergonomics, Fatigue, Prolonged20
standing21
22
23

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Introduction24
In industrial workplaces, a lot of jobs require workers to perform in standing25
position. One of the advantages of standing is that it can provide a large degree of26
freedom to workers for manipulating materials and tools at the workstation. In addition,27
standing position is practical and effective when the workers are operating large28
machines and huge workpieces, reaching of materials and tools, and pushing and pulling29
any excessive loads. For instance, a worker who works at a conventional surface grinding30
machine requires large degree of freedom to push and pull the machine table when31
performing grinding process on the workpiece’s surface. This kind of job nature is nearly32
impossible to be performed in sitting position, so standing is the best option. Standing33
working position also promotes the worker to be more productive and consequently34
contributes to higher productivity for the organization. However, when workers spent a35
long period of time in standing position throughout their working hours, they may36
experience discomfort and muscle fatigue at the end of workday. In the long run, they37
will potentially experience occupational injuries such as work-related musculoskeletal38
disorders, chronic venous insufficiency, and carotid atherosclerosis [1]. A worker is39
considered to be exposed to prolonged standing if he or she spent over fifty percent of the40
total working hours during a full work shift in standing position [2]. Working in standing41
position for an extended period of time has been recognized as a vital contributor to42
decrease workers’ performance in industry. It includes occupational injuries, productivity43
decrement, increased of treatment and medical costs, and lead to subjective discomfort to44
the workers. When workers perform jobs in prolonged standing, static contraction45
occurred particularly in their back and legs, thus resulted in discomfort and muscle46
fatigue [3]. Besides, the employers will also be affected due to lost of revenue in the47

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forms of productivity, workers’ compensation and health treatment costs [4]. For48
example, pain in the lower back due to prolonged standing can affect the ability of a49
worker to perform job that involving flexion or extension postures. This may in turn50
affect the productivity of the worker. In addition, workers who suffered from51
occupational injuries must be referred to clinical experts for health treatment, which52
definitely involve substantial amount of consultancy and medication costs.53
Many ergonomics simulation systems have been introduced to improve54
productivity, comfort and safety of workers in manufacturing workplaces [5], [6]. In55
industrial ergonomics, numerous assessment tools have been suggested for assessing and56
analyzing risk factors associated with prolonged standing. For examples, the Rapid Entire57
Body Assessment (REBA) and guidelines on standing at work can be applied to analyze58
and minimize postural stress due to standing jobs [7], [8]. However, almost all existing59
methods are available in the form of pen and paper-based and do not provide a60
comprehensive assessment on prolonged standing. The methods are time consuming,61
difficult to retrieve electronically, manual data processing, and causes human error when62
performing calculations. In addition, the existing assessment methods are seen as63
individual and isolated tools, hence it is difficult to perform multi assessments64
concurrently [9]. Up till now, innovative best practices and the underlying measurement65
science for assessing and analyzing the risk factors associated with prolonged standing,66
and documenting the results of the computations do not exist. As a consequence, the67
authorized institutions and industry practitioners who are concerned with occupational68
safety and health do not have a reliable measurement technique to assess and analyze the69
risk factors associated with prolonged standing jobs in the workplace. This includes70
proposing solutions to minimize the risk level.71

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To counter the above limitations, this study was aimed at developing a decision72
support system to assess, analyze, and propose solutions to minimize discomfort and73
muscle fatigue associated with prolonged standing at industrial workplaces. Six risk74
factors related to prolonged standing jobs such as working posture, muscle activity,75
standing duration, holding time, whole-body vibration, and indoor air quality [10] were76
considered as the basis to develop the knowledge of the decision support system. These77
risk factors are related to human, machine, and environment at the workplace. All risk78
factors were analyzed individually to obtain their risk levels. Then, the risk levels of each79
risk factor were assigned with multipliers to represent their severity for discomfort and80
fatigue. A strain index arises through multiplicative interactions between the assigned81
multipliers. Then, the developed decision support system proposes alternative solutions to82
minimize the risk levels corresponding to the strain index. In the developed decision83
support system, Ergonomic Workstation model and Decision Support System for84
Prolonged Standing (DSSfPS) model were designed to function as data capturing and85
analysis respectively.86
Materials and methods87
There are three major stages involved to develop the decision support system.88
Stage 1: Knowledge acquisition89
The first stage in developing the decision support system is knowledge acquisition.90
In a decision support system, knowledge can be considered as the ‘brain’ to process input91
data and information that are supplied to the system. The knowledge can be obtained by92
extracting, structuring, and organizing knowledge from one or more sources [11]. In this93
study, the knowledge acquisition was performed to determine the risk factors that94
contribute significantly to discomfort, and muscle fatigue associated with prolonged95
standing jobs. In addition, the ergonomics evaluation tools to analyze the risk factors96

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were also obtained through this stage. This is carried out by obtaining information from97
reliable sources such as published literatures, on-site observations in industries,98
interviews with management staff and production workers, guidelines and standards from99
the authorized organizations, and experts’ opinions.100
Reviews on articles were performed through hard-bound publications such as101
magazines, journals, and guidelines, as well as on-line databases. The main purposes of102
literature review are to attain the risk factors that contribute significantly to discomfort103
and muscle fatigue associated with prolonged standing jobs. Besides, literature review is104
also useful to identify ergonomics evaluation tools and control measures that can be105
applied to assess, analyze, and minimize the discomfort, and muscle fatigue. The106
identified risk factors, methods, and control measures were compiled to be considered in107
the decision support system.108
Series of on-site observations were conducted with three metal stamping companies109
in Malaysia. The main purpose of the on-site observations was to identify risk factors that110
are present in the standing workstations. A video camcorder was used to record the risk111
factors in the workstations. The video camcorder was set close to the workstation to112
record postures, movements and job cycles while workers are performing their jobs. The113
process of video recording took about 30 minutes to ensure all risk factors, working114
practices, and job processes in the workstation are completely recorded. The advantage of115
video recording is that recorded information is easy to replay, stop or pause so that116
postures, movements and job cycles in the workstation can be monitored. In addition, the117
risk factors captured by video recording can be considered as knowledge for the decision118
support system.119
In addition to the on-site observations, interview sessions with management staff120
and production workers have been performed to acquire the personal background and job121

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activities of the workers, discomfort and fatigue experienced by the workers while122
performing jobs in prolonged standing, history of pain and treatment taken, and123
suggestions to improve standing workstations. The Prolonged Standing Questionnaire124
[12] was applied during the interview sessions. Through the interview sessions, the125
authors were able to acquire useful information that could not be obtained during the on-126
site observations such as the solutions applied by the management staff and production127
workers to minimize discomfort and muscle fatigue associated with prolonged standing.128
Authorized organizations such as International Organization for Standardization129
(ISO) and Department of Safety and Health of Malaysia (DOSH) are good sources to130
acquire information on prolonged standing at workplace. These institutions have131
published regulations and guidelines on standing at workplace. Guidelines on standing at132
workplace [8], code of practice for indoor air quality [13], and standards of comfort133
levels due to acceleration values of whole-body vibration [14] were referred to establish134
the knowledge of decision support system.135
Experts include ergonomic practitioners, medical doctors, physiotherapists, safety136
and health engineers, and academicians are the significant contributors to develop the137
knowledge of decision support system. Their opinions and advices were gathered through138
discussions in seminars and conferences. One of the outcomes from the discussion was139
the selection of ergonomics evaluation tools to be applied in the decision support system.140
All risk factors related to discomfort and muscle fatigue associated with prolonged141
standing which are compiled from the abovementioned sources have been categorized142
into three domains namely human, machine and environment. Each risk factor from these143
domains was equipped with ergonomics evaluation tools to analyze and quantify the risk144
levels. For example, risk factor associated with awkward working posture has been145
identified as a vital contributor to discomfort and fatigue. It is categorized under human146

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domain, and the Rapid Entire Body Assessment [7] was applied to analyze the working147
posture when a worker performs jobs in prolonged standing. The process to integrate the148
risk factors and ergonomics evaluation tools will be performed in second stage.149
Stage 2: Knowledge integration150
The second stage continues with integrating all the knowledge obtained through the151
abovementioned sources. All risk factors that contribute significantly to discomfort and152
muscle fatigue associated with prolonged standing are suited with ergonomics evaluation153
tools to quantify the risk levels. Based on the risk factors, Prolonged Standing Strain154
Index (PSSI) was developed. Details of the PSSI development can be found in the recent155
publication [10].156
Stage 3: Ergonomic workstation model and DSSfPS model157
This study deployed the Model Oriented Simultaneous Engineering System158
(MOSES) architecture [9], [15] to develop the decision support system. The decision159
support system has two main models namely Ergonomic Workstation model and160
Decision Support System for Prolonged Standing (DSSfPS) model. The Ergonomic161
Workstation model captures data and information on workplace, worker, working162
posture, muscles activity, standing duration, holding time, whole-body vibration, and163
indoor air quality in the workplaces.164
DSSfPS model on the other hand comprises knowledge and established ergonomics165
evaluation tools to analyze data about risk factors that are captured by Ergonomic166
Workstation model. The DSSfPS model is functioned to quantify risk levels of each risk167
factor, and compute Prolonged Standing Strain Index (PSSI) value. The DSSfPS model168
will suggest alternative solutions with respect to PSSI value to minimize discomfort and169
fatigue while performing jobs in standing workstation. A working memory in DSSfPS170
model is used to save all results from the analysis. Both Ergonomic Workstation model171

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and DSSfPS model are well fitted under Manufacturing Model of MOSES architecture172
(Figure 1).173
Insert FIGURE 1 here174
The Ergonomic Workstation model is represented by a workstation, the place of a175
worker to perform jobs. The data and information captured by the Ergonomic176
Workstation model are then supplied to DSSfPS model for further analysis. The users177
(e.g. ergonomics experts) can apply the outcomes of analysis to perform modification on178
the design of workstation to minimize discomfort and muscle fatigue associated with179
prolonged standing jobs. The communication between both models in the decision180
support system is achieved when information and data captured by the Ergonomics181
Workstation model are supplied to DSSfPS model through an integrated computer182
environment.183
The programming architecture of Ergonomic Workstation model and DSSfPS184
model was developed using Object Oriented Programming (OOP). In addition, Java185
programming language and open source database db4o (Oracle Corporation, USA) was186
used as programming language and database engine respectively. The OOP is a187
programming paradigm uses interactions of class and attribute (sometimes called object)188
to design the computer programs. The advantage of using OOP is that, any modifications189
to the information architecture are required, can be done easily. For example, if a system190
developer wishes to modify the information architecture for certain reasons, he or she can191
manipulate respective class without interfering other classes.192
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Development of ergonomic workstation model194
The information model of Ergonomic Workstation was designed by considering195
workers in their actual workplaces. Usually, a workplace is represented by the hierarchy196
of company, department, and workstation. In the workstation, it consists of at least a197
human (worker), machine, and working environment. When a worker is performing jobs198
in standing position, his or her workstation is considered as a critical point where199
ergonomics principles should be taken into account to minimize discomfort and muscle200
fatigue.201
In the Ergonomic Workstation information model, the relationship of each human-202
machine-environment constituent in a workstation built their own classes represented by203
the clouds as shown in the information structure (Figure 2). In the information structure,204
the primary class of the Ergonomic Workstation is COMPANY. Then, DEPARTMENT205
is assigned as a sub-class of COMPANY. Consecutively, WORKSTATION class is206
created as sub-class of DEPARTMENT. To represent machine and environment207
constituents, whole-body vibration (WBV) and indoor air quality (IAQ) classes are208
created respectively. The WORKER class consists of five sub-classes to represent job209
activity, working posture, muscle activity, standing duration, and holding time. Except210
for job activity, all classes represent factors need to be assessed and analyzed when a211
worker is performing jobs in prolonged standing. Even though there is no ergonomics212
evaluation carried out for the job activity; however, its information is useful to decide213
further analysis. For example, if a worker is exposed to activity of jobs associated with214
‘standing with forward bending’, ergonomics evaluation associated with working posture215
assessment will be carried out.216

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All information on workplace, worker’s profile, and data about risk factors will be217
saved in Ergonomic Workstation model. The data and information will be manipulated218
by the DSSfPS model for further analysis.219
Development of DSSfPS model221
222
The DSSfPS model is used to analyze the risk factors, quantify standing risk level,223
and obtain alternative solutions to improve the workstation design improvement. The224
DSSfPS model plays an important role to provide alternative solutions to design an225
ergonomic standing workstation; therefore its knowledge development should be given a226
high priority to ensure the system can achieve desired performances.227
As depicted in Figure 3, the DSSfPS model has three main mechanisms: a working228
memory, a knowledge base, and an inference engine. In addition, Graphical User229
Interfaces (GUIs) are provided in the system to facilitate communication between the230
decision support system and the users. The relationship between working memory and231
knowledge base in the DSSfPS model is clearly shown in Figure 4.232
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Working memory of the DSSfPS model239
The DSSfPS Model is equipped with eight working memories. Generally, the240
working memories functioned as the media to temporarily store the data and results of241
analysis. Table 1 summarizes the functions of each working memory.242
Insert TABLE 1 here243
244
Knowledge base of the DSSfPS model245
The knowledge base of DSSfPS model serves as a shell to occupy rule sets for the246
ergonomic evaluation tools such as Posture Rule, Muscle Rule, Holding Rule, Standing247
Rule, WBV Rule, and IAQ Rule. In addition, PSSI Rule is also embedded in the248
knowledge base. The rule sets consist of a list of if statements and a list of then conditions249
to process any input data and provide set of alternative solutions to minimize discomfort250
and muscle fatigue associated with prolonged standing. The development of rule sets251
involved two stages. The first stage is assigning each ergonomics evaluation tool into a252
class. Then, the formulation of rules to reach final results is performed in the second253
stage.254
In the first stage, the ergonomics knowledge was assigned to several classes to255
accommodate the following rule sets:256
1. Posture Rule – containing rules for working posture analysis,257
2. Muscle Rule – containing rules for muscles activity analysis,258
3. Holding Rule – containing rules for holding duration analysis,259
4. Standing Rule – containing rules for standing time analysis,260
5. WBV Rule – containing rules for whole-body vibration exposure analysis, and261
6. IAQ Rule – containing rules for indoor air quality analysis.262

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In the second stage, the rule set for PSSI Rule was developed. The PSSI Rule has263
several rules that are used to perform PSSI calculation and obtain recommendation to264
minimize discomfort and muscle fatigue, based on the PSSI value. The development of265
PSSI Rule began with assigning the results of risk factors analysis (in terms of risk266
levels) to multipliers to represent their severity for discomfort and fatigue. A PSSI value267
arises through multiplicative interactions between these multipliers. Then, potential268
solutions to minimize the risk levels were recommended corresponding to the PSSI value.269
Table 2 summarizes the knowledge base of DSSfPS model which include risk factors or270
knowledge, knowledge description, numbers of rules, questions, and alternative answers.271
Insert TABLE 2 here272
273
Inference engine of the DSSfPS model274
In DSSfPS model, an inference engine is used to achieve results (risk levels, PSSI275
value and recommendations) by matching rule sets in the knowledge base and data276
available in the working memory. The method applied to design the inference mechanism277
is forward chaining. Forward chaining works by processing the data first, and keep using278
the rules in knowledge base to draw new conclusions given by those data [16], [17]. This279
study applied forward chaining because it operates via top-down approach which takes280
data available in the working memory then generate results based on satisfied conditions281
of rules in the knowledge base. In DSSfPS model, the inference engine performs these282
functions:283
1. supply background information of worker such as workplace’s profile, personal284
details, job activities, and data about risk factors that have been captured by285
Ergonomic Workstation model to working memory in DSSfPS model,286

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2. search rule sets in knowledge base to be matched with data from the working287
memory to obtain results (risk levels, PSSI value and recommendations), and288
3. retrieve updated working memory database to display outcomes of the analysis.289
The inference engine of DSSfPS model works in three stages: between GUIs and290
working memory, between working memory and knowledge base, and at working291
memory to display the analysis outcomes.292
Graphical user interfaces (GUIs)293
In the decision support system, the Graphical User Interfaces (GUIs) are used as294
communication medium between the user, Ergonomic Workstation model, and DSSfPS295
model. The GUIs were designed using facilities available in the NetBeans IDE 6.8296
(Oracle Corporation, USA). The user provides information from the actual industrial297
workstation such as workplace’s information, worker’s profile, and the data about risk298
factors to the database in Ergonomic Workstation model through the GUIs. In the299
decision support system, ten GUIs have been designed to perform various functions.300
A main menu GUI was designed to enable a user to populate a database in the301
Ergonomic Workstation model. When the program is executed, the main menu GUI will302
appear. In the main menu GUI, there are several menus:303
1. File menu – to exit from the decision support system,304
2. Manage Database menu – to create databases,305
3. Populate Database menu – to provide data and information about the risk factors to306
be analyzed,307
4. Display Information menu – to preview data and results of analysis, and308
5. Help menu – to guide the user about the basic operation of the decision support309
system.310
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Integrating the ergonomic workstation model and DSSfPS model312
Both Ergonomic Workstation model and DSSfPS model have to be integrated in a313
decision support system to enable prospective users to analyze standing jobs in their314
workplaces. To employ the decision support system, a user should create a database for315
Ergonomic Workstation model and a working memory for DSSfPS model. Then, the user316
should supply information on workplace, worker, and data of risk factors from the actual317
workplaces and store them into the database in Ergonomic Workstation model. The318
inference engine retrieves the data and information from the Ergonomic Workstation319
database and sends to the database in working memory. Then, the inference engine320
examines and matches rules embedded in the knowledge base of DSSfPS model with321
data and information from working memory to generate results. The obtained results will322
be saved in the working memory. The inference engine retrieves the saved results in the323
working memory for display purpose.324
Validation of the decision support system325
A validation process was conducted to ensure the knowledge of developed decision326
support system able to perform its functionality with an acceptable level of accuracy. To327
achieve that, this study has performed the validation process through a technical328
validation using two real case studies [18]. According to technical validation, the results329
generated by decision support system should be consistent with the results of330
conventional methods when assessing and analyzing a similar case study [19].331
The validation process was carried out through two on-site observations at a metal332
stamping company in Shah Alam, Malaysia and a footwear manufacturer in Petaling333
Jaya, Malaysia. To validate the knowledge of the developed decision support system,334
twenty six workers (n=13 from each company) were selected as samples of study. Data335
and information about workplace, workstation, worker, and risk factors were acquired336

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directly from the companies. Data on risk factors including working posture, muscles337
activity, standing duration, and holding time were observed at three conditions: high,338
medium, and low risk levels. Data on risk factors associated with whole-body vibration339
(WBV) were measured using a vibration accelerometer (QUEST Technologies, USA) at340
the standing workstation. Additionally, the quantities of carbon dioxide and carbon341
monoxide surrounding the workstation were measured using Indoor Air Quality Probe342
IQ-410 (Gray Wolf, USA) [20]. In the decision support system, all data and information343
were supplied to Ergonomic Workstation Model, and DSSfPS Model analyzed them to344
generate results. The data and information were analyzed based on individual basis. In345
other words, all workers have their own data and information about workplace,346
workstation, worker, and risk factors. Similarly, the data about risk factors were analyzed347
through conventional method, for examples, data about working posture and muscles348
activity were analyzed using Rapid Entire Body Assessment worksheet [7] and Rodgers349
Muscle Fatigue Analysis worksheet [21] respectively.350
Then, this study performed a comparison between the results generated by decision351
support system and conventional method to determine the validity of knowledge in the352
developed decision support system. Table 3 to Table 8 present the comparison results of353
knowledge for the six risk factors (working posture, muscle activity, duration, holding354
time, whole-body vibration, and indoor air quality). The result of validation for the PSSI355
is shown in Table 9. Based on the comparison, it is clearly shown that the developed356
decision support system able to generate results comparable to conventional methods. In357
other words, results from both decision support system and conventional method showed358
a good agreement for all analyzed risk factors.359
Insert TABLE 3 to TABLE 9 here360
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Discussion362
Decision support system has been recognized as one of efficient decision making363
tools in many applications. For example, in medical care, a decision support system that364
provides explicit point of decision making to clinicians has achieved clinician compliance365
rates of 90-95% [22]. This study has developed a decision support system that consists of366
Ergonomic Workstation model and DSSfPS model. Both models were designed using367
OOP. The authors found that OOP is versatile and flexible system especially when368
designing the programming architecture because any modifications and improvement on369
the models which are related to their classes and attributes can be done easily. In other370
words, if the programming architecture has to be modified for certain reasons, the authors371
can manipulate respective class without having to interfere with other classes. This372
advantage enabled the authors to reorganize the programming architecture easily and373
time saving. In addition, designing relationship of a work system and its components374
using OOP is particularly useful for making the designed system easy to be understood375
[23].376
In terms of knowledge base development, this study established the information377
about risk factors by surveying numerous published research works and performing378
onsite-observations at industrial workplaces. The combination of literature review and379
on-site observations has shown an effective way to establish a comprehensive and total380
knowledge base. As a result, information about risk factors related to human-machine-381
environment has been established. Corresponding to each risk factor, this study applied382
reliable sources such as established ergonomics evaluation tools, guidelines, and standard383
for data analysis and decision making. As compared to other decision support system for384
ergonomics application, the acquisition of knowledge is solely referred to literature385
review [24].386

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In the knowledge base, the current study developed the rule sets utilizing if-then387
production rule to execute the knowledge. The if-then production rule is preferred388
because the data and information to be analyzed by the developed decision support389
system requires straight forward manipulations. In many rule sets development, fuzzy390
rule is widely used [25], [26], [27], [28], [29] however, the fuzzy rule is not suitable in391
the current study because its decision-making is not always a matter of black and white; it392
often involves grey areas [30].393
Working memory is a vital component in a decision support system as it is used to394
store data and information supplied by the users. In developed decision support system,395
data and information about workplaces, workers, and risk factors captured by the396
Ergonomic Workstation model and results of analysis performed by DSSfPS model are397
saved in a working memory. When the decision support system is used to perform many398
assessments, the data in working memory become larger and there is a possibility of data399
confliction. This may lead to data manipulation errors. To avoid this, the working400
memory has to be cleaned. To clear the working memory, it should be initialized. This401
study created a function to initialize working memory of DSSfPS model by deleting the402
old data. Interestingly, the deletion process in the working memory of DSSfPS model is403
not affecting the data and information about workplaces, workers, and risk factors that404
are saved in the Ergonomic Workstation model. The program code deletes the results of405
previous assessment in the working memory of DSSfPS model, however, the data and406
information about workplaces, workers, and risk factors are still available in the407
Ergonomic Workstation model. The advantage of this system is that data and information408
stored in the Ergonomic Workstation model could be retrieved when further assessment409
is required.410

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In this study, forward chaining method was used to develop the inference engine.411
The study prefers this method because it works through top-down approach which takes412
data available in the working memory then generates results based on satisfied conditions413
of rules in the knowledge base. For instance, consider rule sets in the class Posture Rule:414
Rule 1: if reba_score = 1 then posture_risk_level = “Negligible”415
Rule 2: if posture_risk_level = “Negligible” then PostureMultiplier = 1416
Rule 3: if PostureMultiplier = 1 then417
PostureRecommendation = “"The posture should be maintained"418
Based on forward chaining method, start with Rule 1 and go on downward (Rule 3) till a419
rule that fires is found. In other words, the forward chaining method is data-driven420
because the initial data are processed first (reba_score, posture_risk_level,421
PostureMultiplier) and keep using the rules to achieve goal (PostureRecommendation).422
Ten GUIs have been equipped in the decision support system for convenient data423
input, analysis, and display. The GUIs are predominantly used to key in data and424
information about workplaces, workers, risk factors including working posture, muscles425
activity, standing duration, holding time, whole-body vibration, and indoor air quality.426
Each data and information is equipped with individual GUI. The GUIs were text-based427
design with several facilities such as informative message, pull-down menus, check428
boxes, radio button, and OK and Cancel buttons. There is no image provided in the GUIs429
because the constraint of GUIs area and difficult to obtain suitable image to represent430
certain data and information. For example, it is difficult to attach vibration image at431
certain frequency and acceleration in the whole-body vibration GUI because the432
quantities are nearly impossible to be captured.433
At the end, the developed decision support system has undergone validation process.434
Through the validation process, this study found that results generated by the decision435

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support system are comparable to conventional method. The advantage of using the436
decision support system is time saving and systematic data manipulation.437
Conclusion438
A decision support system that is specifically used to analyze risk factors, predict439
risk levels due to standing work, and propose recommendations to minimize discomfort440
and fatigue has been developed in this study. This study identified that working posture,441
muscles activity, standing duration, holding time, whole-body vibration, and indoor air442
quality were the risk factors for discomfort and muscles fatigue while workers are443
performing jobs in standing position. Risk levels of the risk factors can be predicted444
through the Prolonged Standing Stress Index (PSSI). The risk factors and the PSSI have445
been modeled in a decision support system to provide a systematic analysis and solutions446
for discomfort and muscle fatigue associated with prolonged standing. Based on the447
results of validation process, this study concluded that the decision support system is448
reliable for assessing, analyzing, and proposing solutions to minimize discomfort and449
muscle fatigue associated with prolonged standing at industrial workplaces. This study450
suggests further efforts such as extensive testing, attractive and user friendly GUI to451
make the decision support system commercially available.452
Acknowledgements453
The authors would like to acknowledge the Ministry of Higher Education of454
Malaysia, the Faculty of Manufacturing Engineering of Universiti Teknikal Malaysia455
Melaka, the Ministry of Science, Technology and Innovation of Malaysia for funding this456
research under e-Science Research Grant (project no.: 06-01-01-SF0258), the Faculty of457
Mechanical Engineering of Universiti Teknologi MARA and Research Management458
Institute of Universiti Teknologi MARA for providing facilities and assistance in459

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conducting this study. Special thank goes to Miyazu (M) Sdn. Bhd. and Kulitkraf Sdn.460
Bhd. for facilitating the workplace surveys.461
462
463
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Table 1: Working memory of DSSfPS Model and functions
Working Memory Function
Worker Memory Compiles data and information on workplace’s profile (company
name, worker’s department, worker’s workstation); worker’s profile
(name, employee number, age, and job activity).
Posture Memory Saves data, information, and result of working posture analysis.
Muscle Memory Saves data and information, and result of muscles activity analysis.
Standing Memory Saves data, and result of standing duration analysis.
Holding Memory Saves data, and result of holding time analysis.
WBV Memory Saves data, and result of whole-body vibration analysis.
IAQ Memory Saves data, and result of indoor air quality analysis.
PSSI Memory Saves result of PSSI and recommendations.

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Table 2: Summary of knowledge base of DSSfPS model
Knowledge Knowledge description
No. of
rule
No. of
question
No. of optional
answer
Working
Posture
• Trunk movement and posture
• Neck movement and posture
• Legs posture and condition
• Upper arms posture
• Lower arms movement
• Wrist movement
• Load mass and force condition
• Coupling condition
• Posture activity
299 16 53
Muscles
activity
• Effort level of neck, shoulders, back,
arms/elbow, wrists/ hands/ fingers,
legs/ knees, ankles/ feet/ toes
• Continuous effort duration of neck,
shoulders, back, arms/elbow, wrists/
hands/ fingers, legs/ knees, ankles/
feet/ toes
• Effort frequency of neck, shoulders,
back, arms/elbow, wrists/ hands/
fingers, legs/ knees, ankles/ feet/ toes
455 21 84
Holding time
• Shoulder height
• Arm reach distance
10 2 10
Standing
duration
• Standing period 3 1 3
WBV
• Frequency of vibration (foot-to-head
direction)
• Acceleration of vibration (foot-to-head
direction)
166 3 200
IAQ
• The quantity of carbon dioxide
• The quantity of carbon monoxide
• The quantity of formaldehyde
• The quantity of respirable particulates
• The quantity of TVOC
11 5 -
PSSI
• To quantify risk level due to
prolonged standing jobs in the
workstation
25 6 22
Total 969 54 372

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Table 3: Validation of knowledge for working posture
Data and information Results (DSS) Results (CM)
Trunk: 0º to 20º flexion
Neck: More than 20º flexion
Legs: Unstable posture
Upper arms: 20º to 45º flexion
Lower arms: Less than 60º flexion
Wrists: More than 15º flexion
Load: Less than 5 kg
Coupling: Fair (hand hold is acceptable)
Activity: Repeated small range action
Medium risk Medium risk
Neck: 0º to 20º flexion and twisting
Legs: Bilateral weight bearing
Lower arms: 60º to 100º flexion
Coupling: Fair (Handhold acceptable)
Activity: Repeated small range actions
Trunk: Upright
Neck: 0º to 20º flexion
Upper arms: 20º extension to 20º flexion
Wrists: 0º to 15º flexion
Coupling: Good (well-fitted handle)
Activity: Body parts are static
Low risk Low risk

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Table 4: Validation of knowledge for muscle activity
Data and information Results (DSS) Results (CM)
Neck effort level: Moderate-head forward about 20º
Neck effort duration: 6 to 20s
Neck effort frequency: 1-5/ min
Shoulders effort level: Light-arm slightly extended
Shoulders effort duration: Less than 6s
Shoulders effort frequency: 1-5/ min
Back effort level: Moderate-forward bending
Back effort duration: 6 to 20s
Back effort frequency: 1-5/ min
Arms/ elbows effort level: Moderate-Moderate force of lifting
Arms/ elbows effort duration: 6 to 20s
Arms/ elbows effort frequency: 1-5/ min
Wrists/ hands/ fingers effort: Moderate- grip with moderate force
Wrists/ hands/ fingers effort duration: 6 to 20s
Wrists/ hands/ fingers effort frequency: 1-5/ min
Legs/ knees effort level: Moderate-bending forward
Legs/ knees effort duration: 6 to 20s
Legs/ knees effort frequency: 1-5/ min
Ankles/ feet/ toes effort level: Moderate-bending forward
Ankles/ feet/ toes effort duration: 6 to 20s
Ankles/ feet/ toes frequency: 1-5/ min
Neck: 0º to 20º flexion and twisting
Coupling: Fair (Handhold acceptable)
Activity: Repeated small range actions
Neck effort : Light-Head turned partly to side and slightly forward
Neck effort duration: Less than 6s
Neck effort frequency: Less than 1/ min
Shoulders effort level: Light-Arm slightly extended
Shoulders effort duration: Less than 6s
Shoulders effort frequency: Less than 1/ min
Back effort level: Moderate-forward bending
Back effort duration: Less than 6s
Back effort frequency: Less than 1/ min
Arms/ elbows effort level: Light-Light lifting
Arms/ elbows effort duration: Less than 6s
Arms/ elbows effort frequency: Less than 1/ min
Wrists/ hands/ fingers effort level: Light-light forces
Wrists/ hands/ fingers effort duration: Less than 6s
Wrists/ hands/ fingers effort frequency: Less than 1/ min
Legs/ knees effort level: Light-standing without bending
Legs/ knees effort duration: Less than 6s
Legs/ knees effort frequency: Less than 1/ min
Ankles/ feet/ toes effort level: Light-standing without bending
Ankles/ feet/ toes effort duration: Less than 6s
Low risk Low risk

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