# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 1 Introduction
Specifically, mathematical models are used to identify pertinent features from provided datasets, which are then employed for prediction of future states, or classification of the existing data into different groups. In the context of predicting differences in physiological conditions, machine learning classification algorithms play a pivotal role [33; 34]. These include (a) decision trees, which partition the feature space into distinct regions based on simple decision rules, are interpretable, and are suitable for tasks with categorical outcomes [35], (b) Support Vector Machines (SVM), which aim to find the hyperplane that best separates different classes while maximizing the margin between them, making them effective for binary classification tasks with high-dimensional feature spaces [36], and (c) ensemble methods such as Random Forests and Gradient Boosting, which combine multiple classifiers to improve predictive performance and robustness [37; 38; 39]. Previous studies have demonstrated the utility of machine learning techniques in detecting fatigue during tasks such as walking, thereby reducing the risk of injury due to overexertion. By analyzing various physiological signals, including body movement [40; 41; 42; 43; 44], electromyography (EMG) signals [45], stability [46], and heart rate [40; 41], machine learning models can accurately classify individuals into fatigued and non-fatigued states. Moreover, incorporating wearable sensors and advanced signal processing techniques enables real-time monitoring of fatigue levels, allowing for timely interventions to prevent overexertion-related injuries. Overall, machine learning-based fatigue detection systems can be helpful in enhancing workplace safety. Past studies on fatigue detection conducted controlled laboratory experiments comprising fatiguing tasks, with fatigue progression tracked using both subjective and objective measurements. Subjective scales, such as the Borg scale, have been widely used to obtain a measure of global fatigue, which has been utilized as a reference for objective data while training fatigue detection models [47; 48]. Thus, using objective data obtained from sensors, such models can predict perceived exertion levels. Meanwhile, one of the most common methods for quantifying localized fatigue is by recording muscle activity usingsurface electromyography (EMG) [43; 49]. In a recent study, Root-Mean-Square envelopes of raw EMG signals were used among the feature set to detect fatigue level during walking tasks [43]. Fatigue is also known to influence postural control and movement coordination, specifically leading to altered movement kinematics (e.g., range of motion, velocity, and jerk) [40; 48; 50]. These changes in kinematic parameters with fatigue progression have been tracked using motion capture systems, including wearable sensors and infrared camera systems [43; 48; 51; 52; 53]. For instance, changes in gait kinematics can serve as reliable indicators of fatigue during walking [42; 54]. Likewise, features such as joint angles, velocity, and acceleration extracted from position data (of 39 markers) obtained from infrared cameras were utilized to predict fatigue during repetitive manual tasks [53]. Alterations in motor control and their resulting variations in body movement due to fatigue are also observed in whole-body stability measures. For instance, deviations in the center of pressure (COP) measured using force plates have been reported to be correlated with fatigue [51; 55; 56; 57]. A combination of multiple sensor systems has also been utilized to improve prediction performance, such as obtaining features from both EMG and motion capture systems [43]. Overall, efforts demonstrate efficacy of biomechanical data acquisition systems in developing objective approaches of fatigue detection. Monitoring overall demands using multiple sensors may be challenging given the current limitations of sensor technologies as well as their intrusive nature, and intricacies of evaluating EXOs in real-world industrial scenarios [6].