Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach

Abstract

Back-Support Industrial Exoskeletons (BSIEs) can be beneficial in reducing the risk of injury due to overexertion during trunk flexion tasks. Most real-world tasks include complex body movements, leading to mixed outcomes that necessitate field-based methods for detecting overall physical demands. Monitoring fatigue can be beneficial in this regard to ensure that benefits of BSIEs are translated to the real world. Our experiment included 14 participants, who performed 30 repetitions of 45° trunk-flexion while assisted by a BSIE, first without fatigue and then at medium-high back fatigue (7/10 in the Borg scale). We extracted 135 features from recorded muscle activity, trunk motion, and whole-body stability across bending, transition, and retraction portions of each trunk-flexion cycle. Four classification algorithms, namely Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), were implemented to assess fatigue prediction. XGB (Accuracy: 86.1%, Recall: 86%, Specificity: 86.3%) was effective in classifying fatigue with data obtained from a single EMG sensor located on the lower back (erector spinae) muscle. Meanwhile, stability measures showed high predictability with both RF (92.9%, 91.9%, 94.1%) and XGB (93.5, 94.1%, 93.1%). Findings demonstrate the success of force plates, and when replaced by pressure insoles, they can facilitate real-world fatigue detection during BSIE-assisted trunk-flexion tasks.