# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 4 Discussion
Therefore, when using EMG systems, multiple repetitions of trunk flexion tasks may be required to accurately classify fatigue level. Meanwhile, looking at trunk motion, it was expected that movement differences between with vs. without fatigue could be clearly seen towards the end-effector (upper back) rather than the hip region. For instance, the upper-body region was found to be most effective in predicting fatigue during sit-to-stand activities [41]. However, the higher importance attributed to acceleration parameters for the hip marker during the retraction phase (RF algorithm) underscores the significance of this phase in trunk motion analysis and its potential correlation with fatigue. Meanwhile, the lower importance values associated with bending parameters suggest that while trunk bending is a critical component of the activity, other phases such as retraction and transition may have greater predictive power for fatigue. This could be because transition and retraction phases require higher effort to work against gravitational forces, especially given the added weight of the BSIE. Looking at stability, the prominence of features related to the mean velocity of the Center of Pressure (COP) during the transition phase suggests that maintaining stability during transitions between different phases of trunk bending is crucial for fatigue prediction (Figure 7). The consistency of top features being associated with the transition and retraction phases across measures of whole-body stability, muscle activity, and trunk motion indicates the importance of these phases in assessing overall stability and fatigue risk during trunk bending activities. Overall, these inferences underscore the multidimensional nature of fatigue prediction during trunk bending activities, emphasizing the importance of exploring diversity in physiological measures and their corresponding features in developing accurate predictive models. Outcomes from this study may be used to develop a portable wearable system, or even a module attachment for BSIEs to indicate fatigued states in their wearers. One example is shown in Figure 8, similar to previously proposed mobile monitoring devices [67; 68]. Our study shows the success of force plate and single-sensor EMG systems for classifying fatigued states during trunk bending tasks assisted by a BSIE. Among these two sensor systems, EMG requires skin preparation, attachments/taping, and may not be optimal for continuous monitoring of workers. On the other hand, force plates can be embedded underneath the floor. While EMG-derived features may offer advantages in detecting muscle fatigue in specific muscle groups, force plate system can provide a comprehensive assessment of overall body movement, reflecting a global measure of fatigue. This shows potential opportunities to replace force-plates with insole pressure sensors that provide similar measures, specifically acceleration, plantar pressure, COP, and GRF at each foot [35; 46]. Our study employed two force plates, each supporting one foot, potentially facilitating a seamless transition to a pressure insole system. This transition could pave the way for more accessible and minimally invasive fatigue detection among BSIE users. The literature shows several studies focused on developing fatigue detection models using machine learning methods. These studies utilize sensor outputs to predict perceived fatigue levels [53; 69]. It is important to acknowledge that such models are trained on datasets generated from experiments, with predicted fatigue labels reflecting respective experimental conditions. Consequently, the model's ability to provide reliable predictions in real-world settings depends on the similarity between the task and the training dataset [41; 43]. This study introduces an EXO-specific model for fatigue detection. Specifically, the machine learning model demonstrated in this article was trained on a dataset derived from a controlled laboratory experiment where participants performed repetitive trunk flexion tasks while wearing a BSIE.