Hidden Markov Modelling And Recognition Of Euler-Based Motion Patterns For Automatically Detecting Risks Factors From The European Assembly Worksheet

Abstract

To prevent work-related musculoskeletal disorders (WMSD) the ergonomists apply manual heuristic methods to determine when the worker is exposed to risk factors. However, these methods require an observer and the results can be subjective. This paper proposes a method to automatically evaluate the ergonomic risk factors when performing a set of postures from the ergonomic assessment worksheet (EAWS). Joint angle motion data have been recorded with a full-body motion capture system. These data modeled the motion patterns of four different risk factors, with the use of hidden Markov models (HMMs). Based on the EAWS, automated scores were assigned by the HMMs and were compared to the scores calculated manually. Because the method proposed here is intrusive and requires expensive equipment, kinematic data from a reduced set of two sensors was also evaluated.

Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition

“…To summarize, highly adaptable and rapidly reconfigurable frameworks with real-time data processing capabilities are necessary to handle the industry's ergonomics challenges while simultaneously ensuring productivity. This paper expands on the authors' prior work on adaptive HRC [24], and automatic ergonomic monitoring [25], [26], [27], with the goal of creating a unified ergonomic and reconfigurable HRC framework.…”

“…The methodology described in a previous study [26] was followed to detect dangerous motions performed in the television assembly tasks. In this study, HMMs were trained to recognize motion primitives with varying ergonomic risk levels according to EAWS.…”

“…All the motion primitives recognized throughout each task were annotated and used to compute each task's mode and average ergonomic score. The ergonomic score was calculated using the equations in [26], and the statistics were used to infer which tasks expose operators to a higher ergonomic risk. These were assigned to the collaborative robot, and only the tasks in the TV assembly were left to the human operators.…”