Predicting Perceived Back Fatigue During Exoskeleton Supported Trunk Bending Tasks using Machine Learning

Abstract

Repetitive trunk flexion tasks performed over long durations can increase low-back injury risk, where Back Support Industrial Exoskeletons (BSIEs) can be beneficial. While BSIEs have shown effectiveness in lab assessments, real-world outcomes have shown variation based on task complexity, necessitating monitoring of physical demands. Fourteen participants performed repetitive trunk BSIE-assisted forward bending and return, without fatigue and then at medium-high fatigue. We recorded muscle activity in low-back and thigh muscles using Electromyography (EMG) and whole-body stability using force plates. Classification algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) were utilized to predict perceived medium-high back fatigue using sensor data. Highest performance was observed with XGB algorithm using data from a single low-back EMG sensor (Accuracy: 86.1%, Recall: 86%), and force plate (93.5, 94.1%). Outcomes of our study can be helpful in developing novel fatigue detection products, benefiting ergonomists in properly implementing BSIEs in industrial scenarios.