# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 4 Discussion
Objective data, obtained through motion capture, EMG, and force plate systems, were used to extract features, which were then labeled based on each participant's perceived exertion for each trunk flexion cycle. Notably, reported perceived fatigue levels may be influenced by the perception of task demands, which is potentially impacted by wearing an EXO. Wearers of EXOs might report lower RPE values due to the assistance provided by the BSIE. For instance, wearing BSIEs has been shown to enhance maximum acceptable limits by 6% [70]. However, it is also plausible that EXO wearers may feel more empowered (even with actuator assistance turned off), and thus, inclined to report lower RPE values than they would without the EXO. A lack of evidence exists regarding how wearing an EXO might influence the perception of fatigue. Conversely, objective measurements derived from sensor readings may also vary. For instance, studies  
Figure 8: Schematic depicting a system for providing real-time fatigue detection while performing trunk flexion tasks. have reported lower peak amplitude and median frequency of EMG signals in the lower back during initial and fatigued states when assisted with EXOs [71; 72]. Similarly, the structural components of the device can impact natural body movement [73], thereby affecting kinematics and stability measures. Consequently, a model developed using training datasets without consideration of EXOs may not be suitable for fatigue detection during EXO-assisted tasks. By developing an EXO-specific model, our study aims to address the potential discrepancies in fatigue level prediction, ensuring that predicted outcomes are more aligned with realistic scenarios. Although our article demonstrates high performance in detecting fatigue, our study is subject to a few limitations. The participants in this study included average-sized adult males, and generalizing findings from this study to a wider population would require recruitment of participants of varying gender, anthropometric measurements, and demographics. To confirm whether outcomes obtained from laboratory-based equipment (e.g., force plate) provide similar results as their portable counterparts, future endeavors will involve obtaining similar measures as force plates, but with a pressure insole system. This would also include assessing the performance of insole systems during real-world trunk bending tasks outside the laboratory settings. The presented findings in this work can be helpful to ergonomists in implementing BSIEs in industrial scenarios. The dataset in this study consisted of 30 cycles of repetitive trunk flexion performed with/without medium-high fatigue. Our next step would include conducting a more comprehensive study by increasing the number of experimental cycles and recruiting a much larger sample size. Future work could also delve into field evaluations, focusing on muscle demands in trunk musculature, such as the trapezius and oblique muscles, especially relevant for tasks involving asymmetric postures. Additionally, exploring lower body kinematics, stability measures based on foot contact area derived from marker positions, and a comparative analysis between the demands imposed on wearers of BSIE across newer and different types of these wearable assistive devices.