Stochastic Modeling for Assessment of Human Perception and Motion Sensing Errors in Ergonomic Analysis

Abstract

Workers in the construction industry are frequently exposed to physically demanding manual tasks with a high level of ergonomic risk. To prevent ergonomic injuries and disorders, posture-based ergonomic evaluation methods, which require inputs describing the worker's posture (e.g.,body joint angles), have been developed and are used widely in practice. However, the reliability of these ergonomic methods has not been investigated fully from the input measurement perspective, as when collected by a human observer or motion capture sensors, and which may inevitably contain measurement errors (e.g.,human perception errors and sensing errors in estimating human postures). Thus, this study examines the imprecision associated with acquiring the required inputs for ergonomic assessment and investigates its impact on the final result of the analysis. The two primary methods of obtaining the inputs of posture-based evaluation tools, i.e.,human observation and recordings of motion sensing devices, were examined, and a stochastic approach was proposed to evaluate the impact of the input errors on the final result of the ergonomic assessment. The findings indicate that the imprecision associated with inputs from both visual observers and sensors have significant impact on the results of ergonomic analysis and should be incorporated into the analysis accordingly. The contribution of this paper allows practitioners and researchers to understand possible ranges of outputs that can be caused by observation and measurement errors, and to determine allowable tolerance of sensing errors required for ergonomic evaluation through stochastic analysis.