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Work 34 (2009) 133148 DOI 10.3233/WOR-2009-0912 IOS Press

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FAST ERGO X A tool for ergonomic auditing and work-related musculoskeletal disorders preventionIsabel L. NunesUniversidade Nova Lisboa, Faculdade de Ciencias e Tecnologia, Departamento de Engenharia Mecanica e Industrial; and Centro de Tecnologia e Sistemas, UNINOVA; Campus de Caparica, 2829-516 Caparica, Portugal Tel.: +351 212 948 567; E-mail: [email protected]

Received 21 February 2008 Accepted 7 May 2009

Abstract. Work-related musculoskeletal disorders associated with repetitive and strenuous working conditions continue to represent one of the biggest occupational problems in companies. Despite the variety of efforts to control them, including engineering design changes, organizational modications and working methods training programs, work-related musculoskeletal disorders account for a huge amount of human suffering and economic costs to companies and to healthcare systems. This paper presents an ergonomic analysis tool, FAST ERGO X, designed to support ergonomic auditing activities related with work-related musculoskeletal disorders. This tool can be used to analyze workplaces regarding potential ergonomic risk factors. The FAST ERGO X is a fuzzy expert system designed to help the identication, assessment and control of the risk factors present in the work system, due to lack of adequate ergonomics. Based on objective and subjective data, the system evaluates the risk factors that can lead to the development of work-related musculoskeletal disorders, and presents the ndings resulting from such evaluation. The system also presents recommendations to eliminate or at least reduce the risk factors present in the work situation under analysis. Keywords: Ergonomic workstation analysis, prevention of ergonomic risk factors, expert systems, fuzzy logics

1. Introduction Work-related musculoskeletal disorders (WRMD) are impairments of bodily structures such as muscles, joints, tendons, ligaments, nerves, bones and the localized blood circulation system, caused or aggravated primarily by work itself or by the environment in which work is implemented. The WRMD are a central concern in Europe, given the increasingly large number of workers affected. WRMD are the main occupational disease category suffered by European workers and they are widespread in all activity sectors. According to the European Foundation for the Improvement of Living and Working Conditions more than one third of the European workers suffer from WRMD [5]. Other factors contributing to the relevance of the subject are the heavy economic

consequences resulting from the high WRMD prevalence and the suffering they cause, often leading to permanent, partial or total disability of the worker. Data from the Nordic countries and the Netherlands, estimate the costs related to WRMD at between 0.5 and 2% of Gross Domestic Product. According to the same data, the WRMD affect women more than men because of the type of work they perform [1]. The recognition that the work may adversely affect health is not new. Almost 300 years ago (in 1717) the Italian physician Bernardino Ramazzini, father of occupational medicine, acknowledged the relationship between work and certain disorders of the musculoskeletal system due to the performance of sudden and irregular movements and the adoption of awkward postures. In old medical records is also possible to nd references to a variety of injuries related to the execution

1051-9815/09/$17.00 2009 IOS Press and the authors. All rights reserved

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I.L. Nunes / FAST ERGO X a tool for ergonomic auditing and WRMD prevention

of certain work. Such disorders assumed names related with the professions where they mainly occurred (for instance carpenters elbow, seamstress wrist or bricklayers shoulder) [28]. Over the years much has been written about these disorders, their incidence and risk factors. See for instance [24,6,810,1416,28,29,33,3537]. The strong correlation between the incidence of WRMD and the exertions resulting from the working conditions is well known, particularly considering the physical risk factors associated with jobs (e.g., awkward postures, high repetition, excessive force, static work, cold or vibration). Work intensication and stress seem also to be factors that increasingly contribute to the onset of those disorders [5]. The Fourth European Working Conditions Survey data revealed that organisational features such as job rotation and team working are associated with the incidence of WRMD [26]. On the other hand, the same survey states that a good level of job autonomy and control over work, support from colleagues and superiors, opportunities to learn new things and worker participation result in lower levels of exposure to WRMD. Along the years different ergonomic tools for assessing workstations in order to identify WRMD risk factors have been developed by individuals and organizations. Some examples are, for instance, OWAS [7] (and the associated software WinOWAS [31]), RULA [11], Strain Index [12], NIOSH [13,34] or OCRA [24,25]. Despite all the available knowledge there remains some uncertainty about the precise level of exposure to risk factors that triggers WRMD. In addition there is signicant variability of individual response to the risk factors exposure. Aware that there was yet room for use of alternative approaches and the development of new features, and recognizing the adequacy of applying fuzzy expert systems for dealing with the uncertainty and imprecision inherent in the factors considered in an ergonomic analysis, the author developed a fuzzy expert system model for workstation ergonomic analysis, named ERGO X, a rst prototype [18,23] and then the FAST ERGO X. The ERGO X method of workstation ergonomic analysis was subject to a Portuguese patent application [21]. FAST ERGO X is a fuzzy expert system designed to identify, evaluate and control the risk factors due to ergonomic inadequacies existing in the work system. Based on objective and subjective data, the system evaluates the risk factors present in workplaces that can lead to the development of WRMD, and presents the ndings of the evaluation. The system also presents

recommendations that users can follow to eliminate or at least reduce the risk factors present in the work situation. This paper contains 5 sections. Section 1 is this introduction, Section 2 introduces some basic concepts about Fuzzy Logics and Expert Systems, Section 3 presents FAST ERGO X features, Section 4 demonstrates the use of the system on the analysis of a workstation; and Section 5 presents the Conclusions.

2. Fuzzy logic and expert systems The development of ergonomic workstation analysis tools is conditioned by the complexity, imprecision and subjectivity that often characterizes the knowledge and data used in the ergonomic analysis process. Fuzzy Logic (Fuzzy Set Theory) provides appropriate logicalmathematical tools to deal with problems with such characteristics [39]. On the other hand, Expert Systems offer support to experts and non-experts in dealing with complex and ill structured problems, such as humancentered systems [32]. 2.1. Fuzzy Logic Fuzzy Logic (FL) foundations were laid, in 1965, by Lot Zadeh with the formulation of Fuzzy Set Theory [39]. FL provides a mathematical framework for the systematic treatment of vagueness and imprecision. The subjective nature of human classication processes renders classical (dichotomous) approaches almost useless to deal with imprecise systems. So FL facilitates the elicitation and encoding of uncertain knowledge. It provides a representation mechanism that improves the exibility for dealing with imprecise data. The result is more robust tools that perform better for a wider variety of conditions and users. From an encoding point of view, fuzzy sets support the representation of knowledge and its uncertainty as a unique entity. The resulting representation is very exible, since it can be easily coupled with non-fuzzy forms of knowledge representation, and it can be manipulated by a variety of evaluation methods [30]. A fuzzy set presents a boundary with a gradual contour (see Fig. 1), by contrast with classical sets, which present a discrete border. Let U be the universe of discourse and u a generic element of U, then U = {u}. A fuzzy set A, dened in U, is one set of the dual pairs: A = {(u, A (u))|u U }

I.L. Nunes / FAST ERGO X a tool for ergonomic auditing and WRMD prevention

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1Membership Deg Degreevery near

near not near

very, quite, more or less)] and connectives (and, or). Each modier has a mathematical function associated to it. For instance, considering the primary term near, the following terms can be generated: very near = near2 A (u)2 not near = 1 near 1 A (u) Illustrative membership functions of the three terms (near, very near, not near) are depicted in Fig. 1. Naturally the distance scale will depend on the context, for instance, if one considers to walk to the school, the distance corresponding the point where near fuzzy set membership degree reaches 0 can be something like 1 km; however if one considers that the travel is done using car or public transportations, then the distance can be something like 10 km. In the example depicted in Fig. 1 it was assumed that the concept of near is totally true for a distance smaller than 200 m (membership degree of 1) and is false for a distance greater than 1 km (membership degree of 0). Distances in between 200 m and 1 km will be relatively near, but the adherence to the near concept will decrease as the distance increases, i.e., the membership degree will tend to 0. Looking at the 3 concepts represented, it is possible to observe that as the distance increases the very near set becomes false faster than the near set, and on the other hand the not near

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