A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design

Despite many years of research efforts, the occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors of LBDs interact in causing the injury, as the nature and mechanism of these disorders are relatively unknown phenomena. The task of an industrial ergonomist is complicated because the potential risk factors that may contribute to the onset of LBDs interact in a complex way, and require an analyst to apply elaborate data measurement and collection techniques for a realistic job analysis. This makes it difficult to discriminate well between the jobs that place workers at high or low risk of LBDs. The main objective of this study was to develop an artificial neural network-based diagnostic system which can classify industrial jobs according to the potential risk for low back disorders due to workplace design. Such a system could be useful in hazard analysis and injury prevention due to manual handling of loads in industrial environments. The results show that the developed diagnostic system can successfully classify jobs into the low and high risk categories of LBDs based on lifting task characteristics.