1 s2.0-s2093791114000262-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 standing
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at workplace
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Isa HALIM, PhD1
, Hambali AREP, PhD1
, Seri Rahayu KAMAT, PhD1
, Rohana
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ABDULLAH, MSc2
, Abdul Rahman OMAR, PhD3
, Ahmad Rasdan ISMAIL, PhD4
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Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian
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Tunggal, Hang Tuah Jaya, Melaka, Malaysia.
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Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, 76100 Durian
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Tunggal, Hang Tuah Jaya, Melaka, Malaysia.
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Faculty of Mechanical Engineering, Universiti Teknologi MARA, 40450 Shah Alam,
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Selangor, Malaysia.
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Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang,
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Malaysia.
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Correspondence to:
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Isa Halim
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Faculty of Manufacturing Engineering
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Universiti Teknikal Malaysia Melaka
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76100 Durian Tunggal, Hang Tuah Jaya, Melaka, Malaysia.
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Tel.: +606-3316501
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Fax: +606-3316411
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e-mail: isa@utem.edu.my
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Running tittle: Decision support system for prolonged standing
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TITLE: Development of decision support system for analysis and solutions of prolonged
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standing at workplace
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ABSTRACT
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Background: Prolonged standing has been hypothesized as one of vital contributors to
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discomfort and muscle fatigue to workers at workplace.
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Objectives: The objective of this study is to develop a decision support system that can
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provide systematic analysis and solutions in minimizing discomfort and muscle fatigue
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associated with prolonged standing.
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Methods: Integration of object oriented programming (OOP) and Model Oriented
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Simultaneous Engineering System (MOSES) were deployed to design the architecture of
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the decision support system.
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Results: Validation of the decision support system was carried out in two manufacturing
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companies. The validation process demonstrated that the decision support system has
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generated reliable results.
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Conclusion: Therefore, this study concluded that the decision support system is a reliable
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advisory tool for analysis and solutions related to discomfort and muscle fatigue
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associated with prolonged standing. The decision support system is suggested to undergo
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further testing for commercialization purpose.
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Keywords: Decision support system, Discomfort, Ergonomics, Fatigue, Prolonged
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standing
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Introduction
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In industrial workplaces, a lot of jobs require workers to perform in standing
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position. One of the advantages of standing is that it can provide a large degree of
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freedom to workers for manipulating materials and tools at the workstation. In addition,
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standing position is practical and effective when the workers are operating large
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machines and huge workpieces, reaching of materials and tools, and pushing and pulling
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any excessive loads. For instance, a worker who works at a conventional surface grinding
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machine requires large degree of freedom to push and pull the machine table when
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performing grinding process on the workpiece’s surface. This kind of job nature is nearly
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impossible to be performed in sitting position, so standing is the best option. Standing
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working position also promotes the worker to be more productive and consequently
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contributes to higher productivity for the organization. However, when workers spent a
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long period of time in standing position throughout their working hours, they may
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experience discomfort and muscle fatigue at the end of workday. In the long run, they
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will potentially experience occupational injuries such as work-related musculoskeletal
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disorders, chronic venous insufficiency, and carotid atherosclerosis [1]. A worker is
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considered to be exposed to prolonged standing if he or she spent over fifty percent of the
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total working hours during a full work shift in standing position [2]. Working in standing
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position for an extended period of time has been recognized as a vital contributor to
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decrease workers’ performance in industry. It includes occupational injuries, productivity
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decrement, increased of treatment and medical costs, and lead to subjective discomfort to
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the workers. When workers perform jobs in prolonged standing, static contraction
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occurred particularly in their back and legs, thus resulted in discomfort and muscle
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fatigue [3]. Besides, the employers will also be affected due to lost of revenue in the
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forms of productivity, workers’ compensation and health treatment costs [4]. For
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example, pain in the lower back due to prolonged standing can affect the ability of a
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worker to perform job that involving flexion or extension postures. This may in turn
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affect the productivity of the worker. In addition, workers who suffered from
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occupational injuries must be referred to clinical experts for health treatment, which
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definitely involve substantial amount of consultancy and medication costs.
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Many ergonomics simulation systems have been introduced to improve
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productivity, comfort and safety of workers in manufacturing workplaces [5], [6]. In
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industrial ergonomics, numerous assessment tools have been suggested for assessing and
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analyzing risk factors associated with prolonged standing. For examples, the Rapid Entire
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Body Assessment (REBA) and guidelines on standing at work can be applied to analyze
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and minimize postural stress due to standing jobs [7], [8]. However, almost all existing
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methods are available in the form of pen and paper-based and do not provide a
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comprehensive assessment on prolonged standing. The methods are time consuming,
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difficult to retrieve electronically, manual data processing, and causes human error when
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performing calculations. In addition, the existing assessment methods are seen as
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individual and isolated tools, hence it is difficult to perform multi assessments
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concurrently [9]. Up till now, innovative best practices and the underlying measurement
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science for assessing and analyzing the risk factors associated with prolonged standing,
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and documenting the results of the computations do not exist. As a consequence, the
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authorized institutions and industry practitioners who are concerned with occupational
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safety and health do not have a reliable measurement technique to assess and analyze the
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risk factors associated with prolonged standing jobs in the workplace. This includes
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proposing solutions to minimize the risk level.
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To counter the above limitations, this study was aimed at developing a decision
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support system to assess, analyze, and propose solutions to minimize discomfort and
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muscle fatigue associated with prolonged standing at industrial workplaces. Six risk
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factors related to prolonged standing jobs such as working posture, muscle activity,
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standing duration, holding time, whole-body vibration, and indoor air quality [10] were
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considered as the basis to develop the knowledge of the decision support system. These
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risk factors are related to human, machine, and environment at the workplace. All risk
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factors were analyzed individually to obtain their risk levels. Then, the risk levels of each
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risk factor were assigned with multipliers to represent their severity for discomfort and
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fatigue. A strain index arises through multiplicative interactions between the assigned
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multipliers. Then, the developed decision support system proposes alternative solutions to
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minimize the risk levels corresponding to the strain index. In the developed decision
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support system, Ergonomic Workstation model and Decision Support System for
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Prolonged Standing (DSSfPS) model were designed to function as data capturing and
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analysis respectively.
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Materials and methods
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There are three major stages involved to develop the decision support system.
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Stage 1: Knowledge acquisition
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The first stage in developing the decision support system is knowledge acquisition.
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In a decision support system, knowledge can be considered as the ‘brain’ to process input
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data and information that are supplied to the system. The knowledge can be obtained by
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extracting, structuring, and organizing knowledge from one or more sources [11]. In this
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study, the knowledge acquisition was performed to determine the risk factors that
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contribute significantly to discomfort, and muscle fatigue associated with prolonged
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standing jobs. In addition, the ergonomics evaluation tools to analyze the risk factors
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were also obtained through this stage. This is carried out by obtaining information from
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reliable sources such as published literatures, on-site observations in industries,
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interviews with management staff and production workers, guidelines and standards from
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the authorized organizations, and experts’ opinions.
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Reviews on articles were performed through hard-bound publications such as
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magazines, journals, and guidelines, as well as on-line databases. The main purposes of
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literature review are to attain the risk factors that contribute significantly to discomfort
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and muscle fatigue associated with prolonged standing jobs. Besides, literature review is
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also useful to identify ergonomics evaluation tools and control measures that can be
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applied to assess, analyze, and minimize the discomfort, and muscle fatigue. The
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identified risk factors, methods, and control measures were compiled to be considered in
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the decision support system.
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Series of on-site observations were conducted with three metal stamping companies
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in Malaysia. The main purpose of the on-site observations was to identify risk factors that
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are present in the standing workstations. A video camcorder was used to record the risk
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factors in the workstations. The video camcorder was set close to the workstation to
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record postures, movements and job cycles while workers are performing their jobs. The
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process of video recording took about 30 minutes to ensure all risk factors, working
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practices, and job processes in the workstation are completely recorded. The advantage of
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video recording is that recorded information is easy to replay, stop or pause so that
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postures, movements and job cycles in the workstation can be monitored. In addition, the
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risk factors captured by video recording can be considered as knowledge for the decision
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support system.
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In addition to the on-site observations, interview sessions with management staff
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and production workers have been performed to acquire the personal background and job
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activities of the workers, discomfort and fatigue experienced by the workers while
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performing jobs in prolonged standing, history of pain and treatment taken, and
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suggestions to improve standing workstations. The Prolonged Standing Questionnaire
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[12] was applied during the interview sessions. Through the interview sessions, the
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authors were able to acquire useful information that could not be obtained during the on-
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site observations such as the solutions applied by the management staff and production
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workers to minimize discomfort and muscle fatigue associated with prolonged standing.
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Authorized organizations such as International Organization for Standardization
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(ISO) and Department of Safety and Health of Malaysia (DOSH) are good sources to
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acquire information on prolonged standing at workplace. These institutions have
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published regulations and guidelines on standing at workplace. Guidelines on standing at
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workplace [8], code of practice for indoor air quality [13], and standards of comfort
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levels due to acceleration values of whole-body vibration [14] were referred to establish
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the knowledge of decision support system.
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Experts include ergonomic practitioners, medical doctors, physiotherapists, safety
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and health engineers, and academicians are the significant contributors to develop the
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knowledge of decision support system. Their opinions and advices were gathered through
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discussions in seminars and conferences. One of the outcomes from the discussion was
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the selection of ergonomics evaluation tools to be applied in the decision support system.
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All risk factors related to discomfort and muscle fatigue associated with prolonged
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standing which are compiled from the abovementioned sources have been categorized
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into three domains namely human, machine and environment. Each risk factor from these
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domains was equipped with ergonomics evaluation tools to analyze and quantify the risk
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levels. For example, risk factor associated with awkward working posture has been
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identified as a vital contributor to discomfort and fatigue. It is categorized under human
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domain, and the Rapid Entire Body Assessment [7] was applied to analyze the working
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posture when a worker performs jobs in prolonged standing. The process to integrate the
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risk factors and ergonomics evaluation tools will be performed in second stage.
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Stage 2: Knowledge integration
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The second stage continues with integrating all the knowledge obtained through the
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abovementioned sources. All risk factors that contribute significantly to discomfort and
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muscle fatigue associated with prolonged standing are suited with ergonomics evaluation
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tools to quantify the risk levels. Based on the risk factors, Prolonged Standing Strain
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Index (PSSI) was developed. Details of the PSSI development can be found in the recent
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publication [10].
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Stage 3: Ergonomic workstation model and DSSfPS model
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This study deployed the Model Oriented Simultaneous Engineering System
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(MOSES) architecture [9], [15] to develop the decision support system. The decision
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support system has two main models namely Ergonomic Workstation model and
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Decision Support System for Prolonged Standing (DSSfPS) model. The Ergonomic
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Workstation model captures data and information on workplace, worker, working
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posture, muscles activity, standing duration, holding time, whole-body vibration, and
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indoor air quality in the workplaces.
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DSSfPS model on the other hand comprises knowledge and established ergonomics
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evaluation tools to analyze data about risk factors that are captured by Ergonomic
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Workstation model. The DSSfPS model is functioned to quantify risk levels of each risk
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factor, and compute Prolonged Standing Strain Index (PSSI) value. The DSSfPS model
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will suggest alternative solutions with respect to PSSI value to minimize discomfort and
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fatigue while performing jobs in standing workstation. A working memory in DSSfPS
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model is used to save all results from the analysis. Both Ergonomic Workstation model
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and DSSfPS model are well fitted under Manufacturing Model of MOSES architecture
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(Figure 1).
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Insert FIGURE 1 here
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The Ergonomic Workstation model is represented by a workstation, the place of a
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worker to perform jobs. The data and information captured by the Ergonomic
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Workstation model are then supplied to DSSfPS model for further analysis. The users
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(e.g. ergonomics experts) can apply the outcomes of analysis to perform modification on
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the design of workstation to minimize discomfort and muscle fatigue associated with
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prolonged standing jobs. The communication between both models in the decision
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support system is achieved when information and data captured by the Ergonomics
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Workstation model are supplied to DSSfPS model through an integrated computer
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environment.
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The programming architecture of Ergonomic Workstation model and DSSfPS
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model was developed using Object Oriented Programming (OOP). In addition, Java
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programming language and open source database db4o (Oracle Corporation, USA) was
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used as programming language and database engine respectively. The OOP is a
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programming paradigm uses interactions of class and attribute (sometimes called object)
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to design the computer programs. The advantage of using OOP is that, any modifications
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to the information architecture are required, can be done easily. For example, if a system
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developer wishes to modify the information architecture for certain reasons, he or she can
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manipulate respective class without interfering other classes.
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Development of ergonomic workstation model
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The information model of Ergonomic Workstation was designed by considering
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workers in their actual workplaces. Usually, a workplace is represented by the hierarchy
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of company, department, and workstation. In the workstation, it consists of at least a
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human (worker), machine, and working environment. When a worker is performing jobs
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in standing position, his or her workstation is considered as a critical point where
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ergonomics principles should be taken into account to minimize discomfort and muscle
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fatigue.
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In the Ergonomic Workstation information model, the relationship of each human-
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machine-environment constituent in a workstation built their own classes represented by
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the clouds as shown in the information structure (Figure 2). In the information structure,
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the primary class of the Ergonomic Workstation is COMPANY. Then, DEPARTMENT
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is assigned as a sub-class of COMPANY. Consecutively, WORKSTATION class is
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created as sub-class of DEPARTMENT. To represent machine and environment
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constituents, whole-body vibration (WBV) and indoor air quality (IAQ) classes are
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created respectively. The WORKER class consists of five sub-classes to represent job
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activity, working posture, muscle activity, standing duration, and holding time. Except
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for job activity, all classes represent factors need to be assessed and analyzed when a
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worker is performing jobs in prolonged standing. Even though there is no ergonomics
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evaluation carried out for the job activity; however, its information is useful to decide
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further analysis. For example, if a worker is exposed to activity of jobs associated with
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‘standing with forward bending’, ergonomics evaluation associated with working posture
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assessment will be carried out.
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All information on workplace, worker’s profile, and data about risk factors will be
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saved in Ergonomic Workstation model. The data and information will be manipulated
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by the DSSfPS model for further analysis.
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Development of DSSfPS model
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The DSSfPS model is used to analyze the risk factors, quantify standing risk level,
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and obtain alternative solutions to improve the workstation design improvement. The
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DSSfPS model plays an important role to provide alternative solutions to design an
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ergonomic standing workstation; therefore its knowledge development should be given a
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high priority to ensure the system can achieve desired performances.
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As depicted in Figure 3, the DSSfPS model has three main mechanisms: a working
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memory, a knowledge base, and an inference engine. In addition, Graphical User
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Interfaces (GUIs) are provided in the system to facilitate communication between the
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decision support system and the users. The relationship between working memory and
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knowledge base in the DSSfPS model is clearly shown in Figure 4.
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Working memory of the DSSfPS model
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The DSSfPS Model is equipped with eight working memories. Generally, the
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working memories functioned as the media to temporarily store the data and results of
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analysis. Table 1 summarizes the functions of each working memory.
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Insert TABLE 1 here
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Knowledge base of the DSSfPS model
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The knowledge base of DSSfPS model serves as a shell to occupy rule sets for the
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ergonomic evaluation tools such as Posture Rule, Muscle Rule, Holding Rule, Standing
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Rule, WBV Rule, and IAQ Rule. In addition, PSSI Rule is also embedded in the
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knowledge base. The rule sets consist of a list of if statements and a list of then conditions
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to process any input data and provide set of alternative solutions to minimize discomfort
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and muscle fatigue associated with prolonged standing. The development of rule sets
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involved two stages. The first stage is assigning each ergonomics evaluation tool into a
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class. Then, the formulation of rules to reach final results is performed in the second
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stage.
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In the first stage, the ergonomics knowledge was assigned to several classes to
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accommodate the following rule sets:
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1. Posture Rule – containing rules for working posture analysis,
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2. Muscle Rule – containing rules for muscles activity analysis,
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3. Holding Rule – containing rules for holding duration analysis,
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4. Standing Rule – containing rules for standing time analysis,
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5. WBV Rule – containing rules for whole-body vibration exposure analysis, and
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6. IAQ Rule – containing rules for indoor air quality analysis.
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In the second stage, the rule set for PSSI Rule was developed. The PSSI Rule has
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several rules that are used to perform PSSI calculation and obtain recommendation to
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minimize discomfort and muscle fatigue, based on the PSSI value. The development of
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PSSI Rule began with assigning the results of risk factors analysis (in terms of risk
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levels) to multipliers to represent their severity for discomfort and fatigue. A PSSI value
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arises through multiplicative interactions between these multipliers. Then, potential
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solutions to minimize the risk levels were recommended corresponding to the PSSI value.
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Table 2 summarizes the knowledge base of DSSfPS model which include risk factors or
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knowledge, knowledge description, numbers of rules, questions, and alternative answers.
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Insert TABLE 2 here
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Inference engine of the DSSfPS model
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In DSSfPS model, an inference engine is used to achieve results (risk levels, PSSI
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value and recommendations) by matching rule sets in the knowledge base and data
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available in the working memory. The method applied to design the inference mechanism
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is forward chaining. Forward chaining works by processing the data first, and keep using
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the rules in knowledge base to draw new conclusions given by those data [16], [17]. This
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study applied forward chaining because it operates via top-down approach which takes
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data available in the working memory then generate results based on satisfied conditions
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of rules in the knowledge base. In DSSfPS model, the inference engine performs these
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functions:
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1. supply background information of worker such as workplace’s profile, personal
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details, job activities, and data about risk factors that have been captured by
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Ergonomic Workstation model to working memory in DSSfPS model,
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2. search rule sets in knowledge base to be matched with data from the working
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memory to obtain results (risk levels, PSSI value and recommendations), and
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3. retrieve updated working memory database to display outcomes of the analysis.
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The inference engine of DSSfPS model works in three stages: between GUIs and
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working memory, between working memory and knowledge base, and at working
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memory to display the analysis outcomes.
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Graphical user interfaces (GUIs)
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In the decision support system, the Graphical User Interfaces (GUIs) are used as
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communication medium between the user, Ergonomic Workstation model, and DSSfPS
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model. The GUIs were designed using facilities available in the NetBeans IDE 6.8
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(Oracle Corporation, USA). The user provides information from the actual industrial
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workstation such as workplace’s information, worker’s profile, and the data about risk
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factors to the database in Ergonomic Workstation model through the GUIs. In the
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decision support system, ten GUIs have been designed to perform various functions.
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A main menu GUI was designed to enable a user to populate a database in the
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Ergonomic Workstation model. When the program is executed, the main menu GUI will
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appear. In the main menu GUI, there are several menus:
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1. File menu – to exit from the decision support system,
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2. Manage Database menu – to create databases,
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3. Populate Database menu – to provide data and information about the risk factors to
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be analyzed,
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4. Display Information menu – to preview data and results of analysis, and
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5. Help menu – to guide the user about the basic operation of the decision support
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system.
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Integrating the ergonomic workstation model and DSSfPS model
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Both Ergonomic Workstation model and DSSfPS model have to be integrated in a
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decision support system to enable prospective users to analyze standing jobs in their
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workplaces. To employ the decision support system, a user should create a database for
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Ergonomic Workstation model and a working memory for DSSfPS model. Then, the user
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should supply information on workplace, worker, and data of risk factors from the actual
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workplaces and store them into the database in Ergonomic Workstation model. The
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inference engine retrieves the data and information from the Ergonomic Workstation
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database and sends to the database in working memory. Then, the inference engine
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examines and matches rules embedded in the knowledge base of DSSfPS model with
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data and information from working memory to generate results. The obtained results will
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be saved in the working memory. The inference engine retrieves the saved results in the
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working memory for display purpose.
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Validation of the decision support system
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A validation process was conducted to ensure the knowledge of developed decision
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support system able to perform its functionality with an acceptable level of accuracy. To
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achieve that, this study has performed the validation process through a technical
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validation using two real case studies [18]. According to technical validation, the results
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generated by decision support system should be consistent with the results of
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conventional methods when assessing and analyzing a similar case study [19].
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The validation process was carried out through two on-site observations at a metal
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stamping company in Shah Alam, Malaysia and a footwear manufacturer in Petaling
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Jaya, Malaysia. To validate the knowledge of the developed decision support system,
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twenty six workers (n=13 from each company) were selected as samples of study. Data
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and information about workplace, workstation, worker, and risk factors were acquired
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directly from the companies. Data on risk factors including working posture, muscles
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activity, standing duration, and holding time were observed at three conditions: high,
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medium, and low risk levels. Data on risk factors associated with whole-body vibration
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(WBV) were measured using a vibration accelerometer (QUEST Technologies, USA) at
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the standing workstation. Additionally, the quantities of carbon dioxide and carbon
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monoxide surrounding the workstation were measured using Indoor Air Quality Probe
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IQ-410 (Gray Wolf, USA) [20]. In the decision support system, all data and information
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were supplied to Ergonomic Workstation Model, and DSSfPS Model analyzed them to
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generate results. The data and information were analyzed based on individual basis. In
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other words, all workers have their own data and information about workplace,
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workstation, worker, and risk factors. Similarly, the data about risk factors were analyzed
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through conventional method, for examples, data about working posture and muscles
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activity were analyzed using Rapid Entire Body Assessment worksheet [7] and Rodgers
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Muscle Fatigue Analysis worksheet [21] respectively.
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Then, this study performed a comparison between the results generated by decision
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support system and conventional method to determine the validity of knowledge in the
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developed decision support system. Table 3 to Table 8 present the comparison results of
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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
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is shown in Table 9. Based on the comparison, it is clearly shown that the developed
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decision support system able to generate results comparable to conventional methods. In
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other words, results from both decision support system and conventional method showed
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a good agreement for all analyzed risk factors.
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Insert TABLE 3 to TABLE 9 here
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Discussion
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Decision support system has been recognized as one of efficient decision making
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tools in many applications. For example, in medical care, a decision support system that
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provides explicit point of decision making to clinicians has achieved clinician compliance
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rates of 90-95% [22]. This study has developed a decision support system that consists of
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Ergonomic Workstation model and DSSfPS model. Both models were designed using
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OOP. The authors found that OOP is versatile and flexible system especially when
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designing the programming architecture because any modifications and improvement on
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the models which are related to their classes and attributes can be done easily. In other
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words, if the programming architecture has to be modified for certain reasons, the authors
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can manipulate respective class without having to interfere with other classes. This
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advantage enabled the authors to reorganize the programming architecture easily and
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time saving. In addition, designing relationship of a work system and its components
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using OOP is particularly useful for making the designed system easy to be understood
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[23].
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In terms of knowledge base development, this study established the information
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about risk factors by surveying numerous published research works and performing
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onsite-observations at industrial workplaces. The combination of literature review and
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on-site observations has shown an effective way to establish a comprehensive and total
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knowledge base. As a result, information about risk factors related to human-machine-
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environment has been established. Corresponding to each risk factor, this study applied
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reliable sources such as established ergonomics evaluation tools, guidelines, and standard
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for data analysis and decision making. As compared to other decision support system for
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ergonomics application, the acquisition of knowledge is solely referred to literature
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review [24].
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In the knowledge base, the current study developed the rule sets utilizing if-then
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production rule to execute the knowledge. The if-then production rule is preferred
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because the data and information to be analyzed by the developed decision support
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system requires straight forward manipulations. In many rule sets development, fuzzy
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rule is widely used [25], [26], [27], [28], [29] however, the fuzzy rule is not suitable in
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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
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store data and information supplied by the users. In developed decision support system,
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data and information about workplaces, workers, and risk factors captured by the
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Ergonomic Workstation model and results of analysis performed by DSSfPS model are
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saved in a working memory. When the decision support system is used to perform many
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assessments, the data in working memory become larger and there is a possibility of data
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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
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study created a function to initialize working memory of DSSfPS model by deleting the
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old data. Interestingly, the deletion process in the working memory of DSSfPS model is
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not affecting the data and information about workplaces, workers, and risk factors that
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are saved in the Ergonomic Workstation model. The program code deletes the results of
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previous assessment in the working memory of DSSfPS model, however, the data and
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information about workplaces, workers, and risk factors are still available in the
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Ergonomic Workstation model. The advantage of this system is that data and information
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stored in the Ergonomic Workstation model could be retrieved when further assessment
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is required.
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In this study, forward chaining method was used to develop the inference engine.
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The study prefers this method because it works through top-down approach which takes
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data available in the working memory then generates results based on satisfied conditions
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of rules in the knowledge base. For instance, consider rule sets in the class Posture Rule:
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Rule 1: if reba_score = 1 then posture_risk_level = “Negligible”
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Rule 2: if posture_risk_level = “Negligible” then PostureMultiplier = 1
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Rule 3: if PostureMultiplier = 1 then
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PostureRecommendation = “"The posture should be maintained"
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Based on forward chaining method, start with Rule 1 and go on downward (Rule 3) till a
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rule that fires is found. In other words, the forward chaining method is data-driven
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because the initial data are processed first (reba_score, posture_risk_level,
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PostureMultiplier) and keep using the rules to achieve goal (PostureRecommendation).
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Ten GUIs have been equipped in the decision support system for convenient data
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input, analysis, and display. The GUIs are predominantly used to key in data and
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information about workplaces, workers, risk factors including working posture, muscles
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activity, standing duration, holding time, whole-body vibration, and indoor air quality.
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Each data and information is equipped with individual GUI. The GUIs were text-based
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design with several facilities such as informative message, pull-down menus, check
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boxes, radio button, and OK and Cancel buttons. There is no image provided in the GUIs
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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
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certain frequency and acceleration in the whole-body vibration GUI because the
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quantities are nearly impossible to be captured.
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At the end, the developed decision support system has undergone validation process.
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Through the validation process, this study found that results generated by the decision
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support system are comparable to conventional method. The advantage of using the
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decision support system is time saving and systematic data manipulation.
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Conclusion
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A decision support system that is specifically used to analyze risk factors, predict
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risk levels due to standing work, and propose recommendations to minimize discomfort
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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
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quality were the risk factors for discomfort and muscles fatigue while workers are
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performing jobs in standing position. Risk levels of the risk factors can be predicted
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through the Prolonged Standing Stress Index (PSSI). The risk factors and the PSSI have
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been modeled in a decision support system to provide a systematic analysis and solutions
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for discomfort and muscle fatigue associated with prolonged standing. Based on the
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results of validation process, this study concluded that the decision support system is
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reliable for assessing, analyzing, and proposing solutions to minimize discomfort and
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muscle fatigue associated with prolonged standing at industrial workplaces. This study
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suggests further efforts such as extensive testing, attractive and user friendly GUI to
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make the decision support system commercially available.
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Acknowledgements
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The authors would like to acknowledge the Ministry of Higher Education of
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Malaysia, the Faculty of Manufacturing Engineering of Universiti Teknikal Malaysia
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Melaka, the Ministry of Science, Technology and Innovation of Malaysia for funding this
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research under e-Science Research Grant (project no.: 06-01-01-SF0258), the Faculty of
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Mechanical Engineering of Universiti Teknologi MARA and Research Management
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Institute of Universiti Teknologi MARA for providing facilities and assistance in
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conducting this study. Special thank goes to Miyazu (M) Sdn. Bhd. and Kulitkraf Sdn.
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Bhd. for facilitating the workplace surveys.
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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
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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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
Low risk Low risk
Low risk Low risk
Low risk Low risk
Arm reach distance: 50% Low risk Low risk
Arm reach distance: 75% Medium risk Medium risk
Arm reach distance: 75% Medium risk Medium risk
Shoulder height:100%

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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
0.315 m/s2
to 0.369 m/s2
Little discomfort Little discomfort
0.374 m/s2
to 0.382 m/s2 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
E
X
P E
R
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P E
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P E
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P E
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IAQ WBV
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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

M
A
N
U
S
C
R
I
P
T
A
C
C
E
P
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E
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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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