Dynamic muscle fatigue assessment using s-EMG technology towards human-centric human-robot collaboration

Abstract

Human-centric human-robot collaboration (HHRC) allows seamless collaboration between humans and robots to fulfill flexible manufacturing operations in a shared workspace while maximizing operator autonomy and well-being toward optimal team performance. Therefore, assessing and monitoring an operator’s physical health, specifically fatigue, is paramount to maintaining a comfortable working environment. However, current fatigue assessment models are not suitable for characterizing the fatigue profile of target muscle groups against repetitive dynamic manufacturing operations non-invasively. To solve this problem, we created a theory for quantifying localized muscular fatigue by just understanding the relative task load and the number of repetitive operations the operator conducted. This was achieved by an experimentally proved multivariable linear relationship between localized muscular fatigue, task load, base muscle strength, and number of repetitive operations for an operation type via s-EMG technologies. We also showed the procedures for developing a personalized muscle fatigue profile for a variety of assembly operations via specialized s-EMG experiment design and measurement. This simple fatigue measurement mechanism allows us to constantly monitor operator fatigue via just monitoring repetitive operations conducted using non-invasive sensors, e.g., cameras. We also provide a framework for integrating our work for online fatigue monitoring in a human-robot collaboration system.