###### Abstract  
Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios. Keywords:ergonomics; evaluation; wearable assistive device; human muscle fatigue; muscle activity; motion analysis; signal processing; machine learning +
Footnote †: journal: Medical  
the in-depth recording of physiological signals. Earlier studies have demonstrated a direct correlation of changes in such signals (heart rate, muscle activity, and body movement) to fatigue, enabling objective fatigue level detection [9; 10; 11]. Monitoring fatigue levels can be helpful in designing task cycles, providing optimum work-rest ratios, and accounting for individual differences, leading to a personalized and effective approach to minimize fatigue in industrial environments. Machine learning classification algorithms have emerged as powerful tools to categorize data into distinct predefined classes. Common types of such algorithms include decision trees, support vector machines, and ensemble methods. Decision trees incorporate a hierarchical tree form with structured nodes (root, decision, and leaf nodes) to categorize datapoints into subsets [12; 13; 14], while Support Vector Machines (SVM) aim to establish a boundary between predefined sets of datapoints [15; 16; 17]. Meanwhile, ensemble methods like Random Forests combine multiple decision trees to improve predictive performance and robustness [18; 19; 20]. Prior studies have implemented these algorithms for detecting fatigue. Common algorithms used for fatigue detection include SVM, Artificial Neural Networks, k-Nearest Neighbors, Random Forest (RF), and Decision Trees [21; 22; 23; 24]. Controlled lab-based studies have been conducted to generate and develop training datasets and evaluate the performance of machine learning models for predicting level of fatigue [25]. To develop objective fatigue detection, models that utilize physiological signals obtained from sensors have been developed. Previous studies have used features calculated from motion data [11; 21; 23; 25], muscle activity [26], balance [22], and variability in heart rate [11; 21]. The accuracy of these machine learning models in correctly classifying the state of an individual as fatigued or non-fatigued has been reported in the range of \(\sim\)70-90%. Thus, machine learning-based systems can be implemented to accurately detect the level of fatigue at workplaces. Exoskeletons (EXOs) are wearable devices that augment the physical capabilities of their wearers using mechanical (passive) or electromechanical (active) actuation and an external structure [27; 28; 29; 30].