## 5 Study Limitations and Future Directions  
To develop the model, we generated a dataset through experimental studies involving both symmetric and asymmetric trunk flexion activities, as asymmetry has been known to elevate physical demands [62; 63]. One limitation of our study is that we solely considered asymmetric bending towards the left side, potentially resulting in slightly different or interchangeable model predictions (left vs. right) depending on the evaluated task. Back sensors were positioned approximately 2 inches apart, and similar outcomes can be anticipated for kinematics measures. However, outcomes may vary for leg muscles, as asymmetry can significantly alter demands on either leg, particularly if features extracted solely from EMG are considered. Therefore, we advise caution when selecting a model presented in this study with a single sensor on either leg (LBF/RBF). In our controlled experiment, participants performed tasks and subjectively reported their fatigue levels while wearing sensors as well as the BSIE. There may be an effect of experimental procedures and sensor placement on the reported perceived fatigue ratings. Future steps can include recording data while performing more complex, real-world activities. Furthermore, future studies should consider a larger as well as a more diverse and inclusive pool of participants, as gender and anthropometric variations can provide a generalizable model.  
Wearable sensors were positioned on the erector spinae and biceps femoris muscles, as previous studies have highlighted the beneficial effects of BSIEs in reducing activity in these muscles [64; 65]. Both regions have also been observed to experience motion alterations due to the structural design of the device [43; 57]. However, it is plausible that the effects of performing tasks with BSIEs can be more pronounced in other regions, such as the extremities (e.g., upper back of the trunk) when considering the kinematics measures, and future studies could evaluate the most prominent sensor locations. Furthermore, although our experiment was centered on back fatigue levels, we did not achieve an equal distribution of datapoints per level, as the rate of fatigue increase varied among individuals. Moreover, the developed model and the experiment conducted in our study involved unloaded activities. However, real-world tasks involving the lifting of items, even lighter loads, can affect body movement and muscle activity measures, which were used as inputs to our developed models. Thus, future work can incorporate more diverse industrial activities.  
We utilized features from all activities within the task cycles, but future work may consider feature reduction by identifying the most prominent activities (static, sustained, dynamic). Additionally, future studies could explore alternative sensor placements, a different set of measures (e.g., whole-body stability, ground reaction forces), and simulate tasks considering asymmetry on the left and right sides. Moreover, it is worth noting that our experiment only encompass RPE levels from 0 (no fatigue) to 7 (medium-high fatigue), as tasks performed at exertion levels exceeding 7 are unlikely in real-world scenarios. Including RPE levels up to level 10 according to the Borg Scale could have potentially enhanced the outcomes presented in this study. Lastly, owing to the controlled nature of our experiment, we recruited participants representing an average adult male. Future studies could involve incorporating a larger and more diverse pool of participants to ensure the generalizability of our fatigue level prediction model to real-world settings.