1 s2.0-s2093791114000250-main

Accepted Manuscript

Development of decision support system for analysis and solutions of prolongedstanding at workplace

Isa Halim, PhD Hambali Arep, PhD Seri Rahayu kamat, PhD Rohana Abdullah, MScAbdul 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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http://dx.doi.org/10.1016/j.shaw.2014.04.002

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ACCEPTED MANUSCRIPTDevelopment of decision support system for analysis and solutions of prolonged standing 1

at workplace 2

3

Isa HALIM, PhD1, Hambali AREP, PhD1, Seri Rahayu KAMAT, PhD1, Rohana 4

ABDULLAH, MSc2, Abdul Rahman OMAR, PhD3, Ahmad Rasdan ISMAIL, PhD4 5

1Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian 6

Tunggal, Hang Tuah Jaya, Melaka, Malaysia. 7

2Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, 76100 Durian 8

Tunggal, Hang Tuah Jaya, Melaka, Malaysia. 9

3Faculty of Mechanical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, 10

Selangor, Malaysia. 11

4Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, 12

Malaysia. 13

14

15

Correspondence to: 16

Isa Halim 17

Faculty of Manufacturing Engineering 18

Universiti Teknikal Malaysia Melaka 19

76100 Durian Tunggal, Hang Tuah Jaya, Melaka, Malaysia. 20

Tel.: +606-3316501 21

Fax: +606-3316411 22

e-mail: [email protected] 23

24

Running tittle: Decision support system for prolonged standing 25

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TITLE: Development of decision support system for analysis and solutions of prolonged 1

standing at workplace 2

ABSTRACT 3

Background: Prolonged standing has been hypothesized as one of vital contributors to 4

discomfort and muscle fatigue to workers at workplace. 5

Objectives: The objective of this study is to develop a decision support system that can 6

provide systematic analysis and solutions in minimizing discomfort and muscle fatigue 7

associated with prolonged standing. 8

Methods: Integration of object oriented programming (OOP) and Model Oriented 9

Simultaneous Engineering System (MOSES) were deployed to design the architecture of 10

the decision support system. 11

Results: Validation of the decision support system was carried out in two manufacturing 12

companies. The validation process demonstrated that the decision support system has 13

generated reliable results. 14

Conclusion: Therefore, this study concluded that the decision support system is a reliable 15

advisory tool for analysis and solutions related to discomfort and muscle fatigue 16

associated with prolonged standing. The decision support system is suggested to undergo 17

further testing for commercialization purpose. 18

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Keywords: Decision support system, Discomfort, Ergonomics, Fatigue, Prolonged 20

standing 21

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Introduction 24

In industrial workplaces, a lot of jobs require workers to perform in standing 25

position. One of the advantages of standing is that it can provide a large degree of 26

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 large 28

machines and huge workpieces, reaching of materials and tools, and pushing and pulling 29

any excessive loads. For instance, a worker who works at a conventional surface grinding 30

machine requires large degree of freedom to push and pull the machine table when 31

performing grinding process on the workpiece’s surface. This kind of job nature is nearly 32

impossible to be performed in sitting position, so standing is the best option. Standing 33

working position also promotes the worker to be more productive and consequently 34

contributes to higher productivity for the organization. However, when workers spent a 35

long period of time in standing position throughout their working hours, they may 36

experience discomfort and muscle fatigue at the end of workday. In the long run, they 37

will potentially experience occupational injuries such as work-related musculoskeletal 38

disorders, chronic venous insufficiency, and carotid atherosclerosis [1]. A worker is 39

considered to be exposed to prolonged standing if he or she spent over fifty percent of the 40

total working hours during a full work shift in standing position [2]. Working in standing 41

position for an extended period of time has been recognized as a vital contributor to 42

decrease workers’ performance in industry. It includes occupational injuries, productivity 43

decrement, increased of treatment and medical costs, and lead to subjective discomfort to 44

the workers. When workers perform jobs in prolonged standing, static contraction 45

occurred particularly in their back and legs, thus resulted in discomfort and muscle 46

fatigue [3]. Besides, the employers will also be affected due to lost of revenue in the 47

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forms of productivity, workers’ compensation and health treatment costs [4]. For 48

example, pain in the lower back due to prolonged standing can affect the ability of a 49

worker to perform job that involving flexion or extension postures. This may in turn 50

affect the productivity of the worker. In addition, workers who suffered from 51

occupational injuries must be referred to clinical experts for health treatment, which 52

definitely involve substantial amount of consultancy and medication costs. 53

Many ergonomics simulation systems have been introduced to improve 54

productivity, comfort and safety of workers in manufacturing workplaces [5], [6]. In 55

industrial ergonomics, numerous assessment tools have been suggested for assessing and 56

analyzing risk factors associated with prolonged standing. For examples, the Rapid Entire 57

Body Assessment (REBA) and guidelines on standing at work can be applied to analyze 58

and minimize postural stress due to standing jobs [7], [8]. However, almost all existing 59

methods are available in the form of pen and paper-based and do not provide a 60

comprehensive assessment on prolonged standing. The methods are time consuming, 61

difficult to retrieve electronically, manual data processing, and causes human error when 62

performing calculations. In addition, the existing assessment methods are seen as 63

individual and isolated tools, hence it is difficult to perform multi assessments 64

concurrently [9]. Up till now, innovative best practices and the underlying measurement 65

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, the 67

authorized institutions and industry practitioners who are concerned with occupational 68

safety and health do not have a reliable measurement technique to assess and analyze the 69

risk factors associated with prolonged standing jobs in the workplace. This includes 70

proposing solutions to minimize the risk level. 71

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To counter the above limitations, this study was aimed at developing a decision 72

support system to assess, analyze, and propose solutions to minimize discomfort and 73

muscle fatigue associated with prolonged standing at industrial workplaces. Six risk 74

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] were 76

considered as the basis to develop the knowledge of the decision support system. These 77

risk factors are related to human, machine, and environment at the workplace. All risk 78

factors were analyzed individually to obtain their risk levels. Then, the risk levels of each 79

risk factor were assigned with multipliers to represent their severity for discomfort and 80

fatigue. A strain index arises through multiplicative interactions between the assigned 81

multipliers. Then, the developed decision support system proposes alternative solutions to 82

minimize the risk levels corresponding to the strain index. In the developed decision 83

support system, Ergonomic Workstation model and Decision Support System for 84

Prolonged Standing (DSSfPS) model were designed to function as data capturing and 85

analysis respectively. 86

Materials and methods 87

There are three major stages involved to develop the decision support system. 88

Stage 1: Knowledge acquisition 89

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 input 91

data and information that are supplied to the system. The knowledge can be obtained by 92

extracting, structuring, and organizing knowledge from one or more sources [11]. In this 93

study, the knowledge acquisition was performed to determine the risk factors that 94

contribute significantly to discomfort, and muscle fatigue associated with prolonged 95

standing jobs. In addition, the ergonomics evaluation tools to analyze the risk factors 96

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were also obtained through this stage. This is carried out by obtaining information from 97

reliable sources such as published literatures, on-site observations in industries, 98

interviews with management staff and production workers, guidelines and standards from 99

the authorized organizations, and experts’ opinions. 100

Reviews on articles were performed through hard-bound publications such as 101

magazines, journals, and guidelines, as well as on-line databases. The main purposes of 102

literature review are to attain the risk factors that contribute significantly to discomfort 103

and muscle fatigue associated with prolonged standing jobs. Besides, literature review is 104

also useful to identify ergonomics evaluation tools and control measures that can be 105

applied to assess, analyze, and minimize the discomfort, and muscle fatigue. The 106

identified risk factors, methods, and control measures were compiled to be considered in 107

the decision support system. 108

Series of on-site observations were conducted with three metal stamping companies 109

in Malaysia. The main purpose of the on-site observations was to identify risk factors that 110

are present in the standing workstations. A video camcorder was used to record the risk 111

factors in the workstations. The video camcorder was set close to the workstation to 112

record postures, movements and job cycles while workers are performing their jobs. The 113

process of video recording took about 30 minutes to ensure all risk factors, working 114

practices, and job processes in the workstation are completely recorded. The advantage of 115

video recording is that recorded information is easy to replay, stop or pause so that 116

postures, movements and job cycles in the workstation can be monitored. In addition, the 117

risk factors captured by video recording can be considered as knowledge for the decision 118

support system. 119

In addition to the on-site observations, interview sessions with management staff 120

and production workers have been performed to acquire the personal background and job 121

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activities of the workers, discomfort and fatigue experienced by the workers while 122

performing jobs in prolonged standing, history of pain and treatment taken, and 123

suggestions to improve standing workstations. The Prolonged Standing Questionnaire 124

[12] was applied during the interview sessions. Through the interview sessions, the 125

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 production 127

workers to minimize discomfort and muscle fatigue associated with prolonged standing. 128

Authorized organizations such as International Organization for Standardization 129

(ISO) and Department of Safety and Health of Malaysia (DOSH) are good sources to 130

acquire information on prolonged standing at workplace. These institutions have 131

published regulations and guidelines on standing at workplace. Guidelines on standing at 132

workplace [8], code of practice for indoor air quality [13], and standards of comfort 133

levels due to acceleration values of whole-body vibration [14] were referred to establish 134

the knowledge of decision support system. 135

Experts include ergonomic practitioners, medical doctors, physiotherapists, safety 136

and health engineers, and academicians are the significant contributors to develop the 137

knowledge of decision support system. Their opinions and advices were gathered through 138

discussions in seminars and conferences. One of the outcomes from the discussion was 139

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 prolonged 141

standing which are compiled from the abovementioned sources have been categorized 142

into three domains namely human, machine and environment. Each risk factor from these 143

domains was equipped with ergonomics evaluation tools to analyze and quantify the risk 144

levels. For example, risk factor associated with awkward working posture has been 145

identified as a vital contributor to discomfort and fatigue. It is categorized under human 146

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domain, and the Rapid Entire Body Assessment [7] was applied to analyze the working 147

posture when a worker performs jobs in prolonged standing. The process to integrate the 148

risk factors and ergonomics evaluation tools will be performed in second stage. 149

Stage 2: Knowledge integration 150

The second stage continues with integrating all the knowledge obtained through the 151

abovementioned sources. All risk factors that contribute significantly to discomfort and 152

muscle fatigue associated with prolonged standing are suited with ergonomics evaluation 153

tools to quantify the risk levels. Based on the risk factors, Prolonged Standing Strain 154

Index (PSSI) was developed. Details of the PSSI development can be found in the recent 155

publication [10]. 156

Stage 3: Ergonomic workstation model and DSSfPS model 157

This study deployed the Model Oriented Simultaneous Engineering System 158

(MOSES) architecture [9], [15] to develop the decision support system. The decision 159

support system has two main models namely Ergonomic Workstation model and 160

Decision Support System for Prolonged Standing (DSSfPS) model. The Ergonomic 161

Workstation model captures data and information on workplace, worker, working 162

posture, muscles activity, standing duration, holding time, whole-body vibration, and 163

indoor air quality in the workplaces. 164

DSSfPS model on the other hand comprises knowledge and established ergonomics 165

evaluation tools to analyze data about risk factors that are captured by Ergonomic 166

Workstation model. The DSSfPS model is functioned to quantify risk levels of each risk 167

factor, and compute Prolonged Standing Strain Index (PSSI) value. The DSSfPS model 168

will suggest alternative solutions with respect to PSSI value to minimize discomfort and 169

fatigue while performing jobs in standing workstation. A working memory in DSSfPS 170

model is used to save all results from the analysis. Both Ergonomic Workstation model 171

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and DSSfPS model are well fitted under Manufacturing Model of MOSES architecture 172

(Figure 1). 173

Insert FIGURE 1 here 174

The Ergonomic Workstation model is represented by a workstation, the place of a 175

worker to perform jobs. The data and information captured by the Ergonomic 176

Workstation model are then supplied to DSSfPS model for further analysis. The users 177

(e.g. ergonomics experts) can apply the outcomes of analysis to perform modification on 178

the design of workstation to minimize discomfort and muscle fatigue associated with 179

prolonged standing jobs. The communication between both models in the decision 180

support system is achieved when information and data captured by the Ergonomics 181

Workstation model are supplied to DSSfPS model through an integrated computer 182

environment. 183

The programming architecture of Ergonomic Workstation model and DSSfPS 184

model was developed using Object Oriented Programming (OOP). In addition, Java 185

programming language and open source database db4o (Oracle Corporation, USA) was 186

used as programming language and database engine respectively. The OOP is a 187

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 modifications 189

to the information architecture are required, can be done easily. For example, if a system 190

developer wishes to modify the information architecture for certain reasons, he or she can 191

manipulate respective class without interfering other classes. 192

193

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Development of ergonomic workstation model 194

The information model of Ergonomic Workstation was designed by considering 195

workers in their actual workplaces. Usually, a workplace is represented by the hierarchy 196

of company, department, and workstation. In the workstation, it consists of at least a 197

human (worker), machine, and working environment. When a worker is performing jobs 198

in standing position, his or her workstation is considered as a critical point where 199

ergonomics principles should be taken into account to minimize discomfort and muscle 200

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 by 203

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, DEPARTMENT 205

is assigned as a sub-class of COMPANY. Consecutively, WORKSTATION class is 206

created as sub-class of DEPARTMENT. To represent machine and environment 207

constituents, whole-body vibration (WBV) and indoor air quality (IAQ) classes are 208

created respectively. The WORKER class consists of five sub-classes to represent job 209

activity, working posture, muscle activity, standing duration, and holding time. Except 210

for job activity, all classes represent factors need to be assessed and analyzed when a 211

worker is performing jobs in prolonged standing. Even though there is no ergonomics 212

evaluation carried out for the job activity; however, its information is useful to decide 213

further analysis. For example, if a worker is exposed to activity of jobs associated with 214

‘standing with forward bending’, ergonomics evaluation associated with working posture 215

assessment will be carried out. 216

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All information on workplace, worker’s profile, and data about risk factors will be 217

saved in Ergonomic Workstation model. The data and information will be manipulated 218

by the DSSfPS model for further analysis. 219


Development of DSSfPS model 221

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. The 224

DSSfPS model plays an important role to provide alternative solutions to design an 225

ergonomic standing workstation; therefore its knowledge development should be given a 226

high priority to ensure the system can achieve desired performances. 227

As depicted in Figure 3, the DSSfPS model has three main mechanisms: a working 228

memory, a knowledge base, and an inference engine. In addition, Graphical User 229

Interfaces (GUIs) are provided in the system to facilitate communication between the 230

decision support system and the users. The relationship between working memory and 231

knowledge base in the DSSfPS model is clearly shown in Figure 4. 232



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Working memory of the DSSfPS model 239

The DSSfPS Model is equipped with eight working memories. Generally, the 240

working memories functioned as the media to temporarily store the data and results of 241

analysis. Table 1 summarizes the functions of each working memory. 242

Insert TABLE 1 here 243

244

Knowledge base of the DSSfPS model 245

The knowledge base of DSSfPS model serves as a shell to occupy rule sets for the 246

ergonomic evaluation tools such as Posture Rule, Muscle Rule, Holding Rule, Standing 247

Rule, WBV Rule, and IAQ Rule. In addition, PSSI Rule is also embedded in the 248

knowledge base. The rule sets consist of a list of if statements and a list of then conditions 249

to process any input data and provide set of alternative solutions to minimize discomfort 250

and muscle fatigue associated with prolonged standing. The development of rule sets 251

involved two stages. The first stage is assigning each ergonomics evaluation tool into a 252

class. Then, the formulation of rules to reach final results is performed in the second 253

stage. 254

In the first stage, the ergonomics knowledge was assigned to several classes to 255

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, and 261

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 has 263

several rules that are used to perform PSSI calculation and obtain recommendation to 264

minimize discomfort and muscle fatigue, based on the PSSI value. The development of 265

PSSI Rule began with assigning the results of risk factors analysis (in terms of risk 266

levels) to multipliers to represent their severity for discomfort and fatigue. A PSSI value 267

arises through multiplicative interactions between these multipliers. Then, potential 268

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 or 270

knowledge, knowledge description, numbers of rules, questions, and alternative answers. 271

Insert TABLE 2 here 272

273

Inference engine of the DSSfPS model 274

In DSSfPS model, an inference engine is used to achieve results (risk levels, PSSI 275

value and recommendations) by matching rule sets in the knowledge base and data 276

available in the working memory. The method applied to design the inference mechanism 277

is forward chaining. Forward chaining works by processing the data first, and keep using 278

the rules in knowledge base to draw new conclusions given by those data [16], [17]. This 279

study applied forward chaining because it operates via top-down approach which takes 280

data available in the working memory then generate results based on satisfied conditions 281

of rules in the knowledge base. In DSSfPS model, the inference engine performs these 282

functions: 283

1. supply background information of worker such as workplace’s profile, personal 284

details, job activities, and data about risk factors that have been captured by 285

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 working 287

memory to obtain results (risk levels, PSSI value and recommendations), and 288

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 and 290

working memory, between working memory and knowledge base, and at working 291

memory to display the analysis outcomes. 292

Graphical user interfaces (GUIs) 293

In the decision support system, the Graphical User Interfaces (GUIs) are used as 294

communication medium between the user, Ergonomic Workstation model, and DSSfPS 295

model. The GUIs were designed using facilities available in the NetBeans IDE 6.8 296

(Oracle Corporation, USA). The user provides information from the actual industrial 297

workstation such as workplace’s information, worker’s profile, and the data about risk 298

factors to the database in Ergonomic Workstation model through the GUIs. In the 299

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 the 301

Ergonomic Workstation model. When the program is executed, the main menu GUI will 302

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 to 306

be analyzed, 307

4. Display Information menu – to preview data and results of analysis, and 308

5. Help menu – to guide the user about the basic operation of the decision support 309

system. 310

311

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Integrating the ergonomic workstation model and DSSfPS model 312

Both Ergonomic Workstation model and DSSfPS model have to be integrated in a 313

decision support system to enable prospective users to analyze standing jobs in their 314

workplaces. To employ the decision support system, a user should create a database for 315

Ergonomic Workstation model and a working memory for DSSfPS model. Then, the user 316

should supply information on workplace, worker, and data of risk factors from the actual 317

workplaces and store them into the database in Ergonomic Workstation model. The 318

inference engine retrieves the data and information from the Ergonomic Workstation 319

database and sends to the database in working memory. Then, the inference engine 320

examines and matches rules embedded in the knowledge base of DSSfPS model with 321

data and information from working memory to generate results. The obtained results will 322

be saved in the working memory. The inference engine retrieves the saved results in the 323

working memory for display purpose. 324

Validation of the decision support system 325

A validation process was conducted to ensure the knowledge of developed decision 326

support system able to perform its functionality with an acceptable level of accuracy. To 327

achieve that, this study has performed the validation process through a technical 328

validation using two real case studies [18]. According to technical validation, the results 329

generated by decision support system should be consistent with the results of 330

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 metal 332

stamping company in Shah Alam, Malaysia and a footwear manufacturer in Petaling 333

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. Data 335

and information about workplace, workstation, worker, and risk factors were acquired 336

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directly from the companies. Data on risk factors including working posture, muscles 337

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 vibration 339

(WBV) were measured using a vibration accelerometer (QUEST Technologies, USA) at 340

the standing workstation. Additionally, the quantities of carbon dioxide and carbon 341

monoxide surrounding the workstation were measured using Indoor Air Quality Probe 342

IQ-410 (Gray Wolf, USA) [20]. In the decision support system, all data and information 343

were supplied to Ergonomic Workstation Model, and DSSfPS Model analyzed them to 344

generate results. The data and information were analyzed based on individual basis. In 345

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 analyzed 347

through conventional method, for examples, data about working posture and muscles 348

activity were analyzed using Rapid Entire Body Assessment worksheet [7] and Rodgers 349

Muscle Fatigue Analysis worksheet [21] respectively. 350

Then, this study performed a comparison between the results generated by decision 351

support system and conventional method to determine the validity of knowledge in the 352

developed decision support system. Table 3 to Table 8 present the comparison results of 353

knowledge for the six risk factors (working posture, muscle activity, duration, holding 354

time, whole-body vibration, and indoor air quality). The result of validation for the PSSI 355

is shown in Table 9. Based on the comparison, it is clearly shown that the developed 356

decision support system able to generate results comparable to conventional methods. In 357

other words, results from both decision support system and conventional method showed 358

a good agreement for all analyzed risk factors. 359

Insert TABLE 3 to TABLE 9 here 360

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Discussion 362

Decision support system has been recognized as one of efficient decision making 363

tools in many applications. For example, in medical care, a decision support system that 364

provides explicit point of decision making to clinicians has achieved clinician compliance 365

rates of 90-95% [22]. This study has developed a decision support system that consists of 366

Ergonomic Workstation model and DSSfPS model. Both models were designed using 367

OOP. The authors found that OOP is versatile and flexible system especially when 368

designing the programming architecture because any modifications and improvement on 369

the models which are related to their classes and attributes can be done easily. In other 370

words, if the programming architecture has to be modified for certain reasons, the authors 371

can manipulate respective class without having to interfere with other classes. This 372

advantage enabled the authors to reorganize the programming architecture easily and 373

time saving. In addition, designing relationship of a work system and its components 374

using OOP is particularly useful for making the designed system easy to be understood 375

[23]. 376

In terms of knowledge base development, this study established the information 377

about risk factors by surveying numerous published research works and performing 378

onsite-observations at industrial workplaces. The combination of literature review and 379

on-site observations has shown an effective way to establish a comprehensive and total 380

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 applied 382

reliable sources such as established ergonomics evaluation tools, guidelines, and standard 383

for data analysis and decision making. As compared to other decision support system for 384

ergonomics application, the acquisition of knowledge is solely referred to literature 385

review [24]. 386

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In the knowledge base, the current study developed the rule sets utilizing if-then 387

production rule to execute the knowledge. The if-then production rule is preferred 388

because the data and information to be analyzed by the developed decision support 389

system requires straight forward manipulations. In many rule sets development, fuzzy 390

rule is widely used [25], [26], [27], [28], [29] however, the fuzzy rule is not suitable in 391

the current study because its decision-making is not always a matter of black and white; it 392

often involves grey areas [30]. 393

Working memory is a vital component in a decision support system as it is used to 394

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 the 396

Ergonomic Workstation model and results of analysis performed by DSSfPS model are 397

saved in a working memory. When the decision support system is used to perform many 398

assessments, the data in working memory become larger and there is a possibility of data 399

confliction. This may lead to data manipulation errors. To avoid this, the working 400

memory has to be cleaned. To clear the working memory, it should be initialized. This 401

study created a function to initialize working memory of DSSfPS model by deleting the 402

old data. Interestingly, the deletion process in the working memory of DSSfPS model is 403

not affecting the data and information about workplaces, workers, and risk factors that 404

are saved in the Ergonomic Workstation model. The program code deletes the results of 405

previous assessment in the working memory of DSSfPS model, however, the data and 406

information about workplaces, workers, and risk factors are still available in the 407

Ergonomic Workstation model. The advantage of this system is that data and information 408

stored in the Ergonomic Workstation model could be retrieved when further assessment 409

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 takes 412

data available in the working memory then generates results based on satisfied conditions 413

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 = 1 416

Rule 3: if PostureMultiplier = 1 then 417

PostureRecommendation = “"The posture should be maintained" 418

Based on forward chaining method, start with Rule 1 and go on downward (Rule 3) till a 419

rule that fires is found. In other words, the forward chaining method is data-driven 420

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 data 423

input, analysis, and display. The GUIs are predominantly used to key in data and 424

information about workplaces, workers, risk factors including working posture, muscles 425

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-based 427

design with several facilities such as informative message, pull-down menus, check 428

boxes, radio button, and OK and Cancel buttons. There is no image provided in the GUIs 429

because the constraint of GUIs area and difficult to obtain suitable image to represent 430

certain data and information. For example, it is difficult to attach vibration image at 431

certain frequency and acceleration in the whole-body vibration GUI because the 432

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 decision 435

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support system are comparable to conventional method. The advantage of using the 436

decision support system is time saving and systematic data manipulation. 437

Conclusion 438

A decision support system that is specifically used to analyze risk factors, predict 439

risk levels due to standing work, and propose recommendations to minimize discomfort 440

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 air 442

quality were the risk factors for discomfort and muscles fatigue while workers are 443

performing jobs in standing position. Risk levels of the risk factors can be predicted 444

through the Prolonged Standing Stress Index (PSSI). The risk factors and the PSSI have 445

been modeled in a decision support system to provide a systematic analysis and solutions 446

for discomfort and muscle fatigue associated with prolonged standing. Based on the 447

results of validation process, this study concluded that the decision support system is 448

reliable for assessing, analyzing, and proposing solutions to minimize discomfort and 449

muscle fatigue associated with prolonged standing at industrial workplaces. This study 450

suggests further efforts such as extensive testing, attractive and user friendly GUI to 451

make the decision support system commercially available. 452

Acknowledgements 453

The authors would like to acknowledge the Ministry of Higher Education of 454

Malaysia, the Faculty of Manufacturing Engineering of Universiti Teknikal Malaysia 455

Melaka, the Ministry of Science, Technology and Innovation of Malaysia for funding this 456

research under e-Science Research Grant (project no.: 06-01-01-SF0258), the Faculty of 457

Mechanical Engineering of Universiti Teknologi MARA and Research Management 458

Institute of Universiti Teknologi MARA for providing facilities and assistance in 459

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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

Trunk: 0º to 20º flexion Neck: 0º to 20º flexion and twisting Legs: Bilateral weight bearing Upper arms: 45º to 90º flexion Lower arms: 60º to 100º flexion Wrists: More than 15º flexion Load: Less than 5 kg Coupling: Fair (Handhold acceptable) Activity: Repeated small range actions


Trunk: Upright Neck: 0º to 20º flexion Legs: Bilateral weight bearing Upper arms: 20º extension to 20º flexion Lower arms: 60º to 100º flexion Wrists: 0º to 15º flexion Load: Less than 5 kg 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


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


Trunk: 0º to 20º flexion Neck: 0º to 20º flexion and twisting Legs: Bilateral weight bearing Upper arms: 45º to 90º flexion Lower arms: 60º to 100º flexion Wrists: More than 15º flexion Load: Less than 5 kg 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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Table 5: Validation of knowledge for standing duration


More than 1 hour of continuous standing, or more than 4 hrs in total


More than 1 hour of continuous standing, and more than 4 hrs in total

High risk High risk

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Table 6: Validation of knowledge for holding time


Shoulder height: 50% Arm reach distance: 25%

Low risk Low risk

Shoulder height: 50%

Arm reach distance: 50%

Low risk Low risk


Low risk Low risk


Low risk Low risk



Shoulder height: 100% Arm reach distance: 50% Low risk Low risk

Shoulder height: 50% Arm reach distance: 75% Medium risk Medium risk

Shoulder height: 75% Arm reach distance: 75% Medium risk Medium risk

Shoulder height:100% Arm reach distance: 75%


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Table 7: Validation of knowledge for whole-body vibration


Acceleration between 0.100 m/s2 to 0.214 m/s2

Comfort Comfort


Little discomfort Little discomfort


Fairly discomfort Fairly discomfort

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Table 8: Validation of knowledge for indoor air quality


Carbon dioxide: 4045 ppm Safe Safe

Carbon monoxide: 1.67 ppm Safe Safe

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Table 9: Validation for PSSI

Risk factors Risk level Rating criterion Multiplier Results (Manual) Results (DSS)

Working posture Low Safe 2 2x1x244x1x3x1

= 1464

Muscle activity Low Little fatigue 1 1464

Standing duration High Unsafe 244

Holding time Low Comfort 1

Whole-body vibration

Little discomfort

Little discomfort

3

Indoor air quality Safe Safe 1

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Figure 1: An integrated environment between Ergonomic Workstation model and

DSSfPS model within MOSES architecture

INTEGRATED ENVIRONMENT

DESIGN FOR FUNCTION

MANUFACTURING CODE GENERATION

MANUFACTURING MODEL PRODUCT MODEL

DESIGN FOR MANUFACTURE

DSSfPS

Ergonomic Workstation

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Figure 2: Information structure in the Ergonomic Workstation model to represent human-

machine-environment in a workstation

COMPANY

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

DEPARTMENT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

WORKSTATION

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

WORKER IAQ WBV

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

EXP ERT

JOB ACTIVITY STANDING

DURATION

HOLDING TIME

MUSCLES ACTIVITY

WORKING POSTURE

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Figure 3: DSSfPS model and its mechanisms

Existing Workstation

Design

Ergonomics Experts

Improved Workstation

Design

DSSfPS Model

Working Memory

Knowledge Base

Inference Engine

Analysis and Design of Standing Workstation

GUIs

Ergonomics Info database

Ergonomics Workstation

Model

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Figure 4: Structure of knowledge base and working memory in the DSSfPS model

DSSfPS

Posture Memory

Worker Memory

Muscle Rule

Posture Rule

Holding Rule

IAQ Rule

IAQ Memory

KNOWLEDGE BASE

WBV Rule

PSSI Rule

Holding Memory

WORKING MEMORY

PSSI Memory

Muscle Memory

Standing Memory

WBV Memory

Standing Rule

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