Isometric-Based Approach for Detecting Localized Muscular Fatigue during Complex Dynamic Manufacturing Operations

Abstract

Unstructured manufacturing environments with flexible production procedures will continue to require worker involvement due to the difficulty of automation. In the context of human-centricity, human physical well-being during manufacturing operations, in particular, muscular fatigue, must be reliably assessed and optimized on the shop floor. However, there is a lack of accurate and reliable methods of dynamic fatigue assessment for complex worker operations within manufacturing environments. To this end, we develop a novel solution for a dynamic fatigue assessment framework to determine the fatigue impact of complex manufacturing operations. The framework utilizes a fatigue index profile that defines the fatigue development trend of an individuals’ target muscle group and alongside the force-generating capacity of the muscle group under the dynamic target operations. By inducing fatigue buildup through the dynamic operations, impact the force-generating capacity, and therefore, the localized muscle fatigue can be determined through the isometric contraction phase. Results indicate that analyzing isometric contractions during complex, dynamic operations can provide a reliable fatigue impact estimation. Furthermore, this method maximizes signal uniformity by leveraging the isometric contraction’s static nature while centering the fatigue buildup on the dynamic target operations. Although the proposed method was trialed using a dynamic vertical handling operation, the framework can determine the fatigue impact of more complex manufacturing operations with minimal deviation to the presented methods. © 2021 IEEE.

Publication
IEEE International Conference on Automation Science and Engineering
Saahil Chand
Saahil Chand
PhD Student

My research interests include distributed robotics, mobile computing and programmable matter.

Yuqian Lu
Yuqian Lu
Principle Investigator / Senior Lecturer

My research interests include smart manufacturing systems, industrial AI and robotics.