## 4 Discussion
### Performance Variation across Fatigue Level Grouping Methods  
The literature shows variation in fatigue prediction accuracy using wearable sensors depending highly on evaluated tasks and the type of classification algorithm utilized. For instance, one study demonstrated accuracy up to \(\sim\)95% using polynomial kernel-based SVM in predicting low and high fatigue during repetitive lifting, push/pull, and carrying tasks [21]. Similarly, fatigued states (no fatigue vs. fatigue) while walking were detected at an accuracy of 90% [25], while tertiary fatigue levels (low, medium, high) were detected during sit-to-stand tasks at an accuracy of 82% [11]. Thus, the prediction accuracy for binary fatigue-level classification in our study was 3% higher in the best performing model (Model A), while the same for tertiary classification was \(\sim\)8% lower when compared to a similar prior work [11]. However, differences in task, equipment, and application need to be considered when directly comparing the outcomes with earlier studies. Analysis of the models in our study shows that performance decreased with tertiary vs. binary classification. Specifically, our selected models (Table 5) show \(\sim\)15-20% difference in accuracy between top performance models between tertiary and binary grouping.  
We observed that the selection of fatigue levels (low/medium/high) based on RPE ratings varied in the literature. For instance, one study assigned 1-3 (low), 4-6 (medium), and 7-9 (high) on a CR-10 scale [11], while another selected <6 (no fatigue), 7-11 (low), 12-16 (medium), and >17 (high) on a CR-20 scale [22]. In addition to using the default grouping of 1-3 (low) and 4-6 (medium), we also explored the effects of variation in the fatigue level grouping method on prediction outcomes. Findings indicate that grouping method 2, which included <2 (low) and 2-6 (medium), led to the highest prediction performance for both the back and leg regions (Figure 5). This indicates that differences in objectively collected data were greater between low and medium fatigue levels when they were pooled together, according to FB2G2/FL2G2 methods vs. other methods. This also means that a fatigue level of 3 corresponded more to a medium fatigue than a low fatigue level. Such differences may have occurred due to subjective bias in reported fatigue levels, possibly caused by wearing the BSIE (such as perceiving a lower fatigue level than actual).  
The fatiguing tasks chosen for this study comprised intermittent trunk-flexion cycles with sustained bending and short relaxation breaks (15 s intervals), reflecting the typical pattern of real-world tasks involving static, sustained, and dynamic activities. Our analysis revealed prediction accuracies of 95%, 82%, and 74% for the binary grouping of leg fatigue and back fatigue, respectively, indicating the feasibility of the top-performing models in detecting fatigue levels. A detailed examination of the models revealed that both binary models were more effective in predicting medium fatigue levels (see Figure 5). These findings align with the study's objectives, as detecting medium fatigue is critical and its presence may necessitate intervention to prevent further fatigue escalation. Conversely, tertiary models exhibited larger samples of the high fatigue class being predicted as low fatigue, which could be detrimental. Therefore, preference should be given to binary grouping models, with caution exercised when using tertiary grouping models.