###### Abstract
Within the past decade, EXOs have risen as promising interventions to improve human performance and safety in the military [31], healthcare [32; 33], and industry [34]. These devices are often categorized based on the body region they support, with common types being those supporting the upper body (back, shoulder, arm, wrist) or lower body (knee, ankle) regions. The industrial variants of EXOs aim to reduce the risk of injury due to overexertion [35]. For instance, the low back stands out as a particularly vulnerable region of the human body, and the annual rate of injury in workplaces has been reported to be \(\sim\)17% across the U.S [36; 37; 38]. Back-Support Industrial Exoskeletons (BSIEs) belong to upper-body EXOs that support the wearer's torso while they perform trunk-bending activities, aiming to decrease muscle activity in the low back muscles [39; 40; 41; 42; 43; 44]. Controlled lab-based and field evaluations of EXOs are conducted by recruiting human subjects, which assists in their design and development [45; 46; 47]. Findings from laboratory evaluations demonstrate decreases in back muscle activity (\(\sim\)8-50%) with the use of BSIEs while performing static bending [39; 40; 43; 48; 49] and dynamic lifting tasks [43; 50; 51; 52; 53; 54]. However, when these devices are implemented in field scenarios, their effects are reported as mixed [55]. To check whether these devices are providing their intended benefits to wearers, there is a need for developing approaches for detecting and monitoring physical demands. Monitoring fatigue during EXO-assisted tasks can be beneficial in such cases. In our earlier study, we developed a model to detect medium-high level of fatigue in the back region during repeated trunk bending/retraction using optoelectronic motion capture, force plates, and muscle activity detection systems [56]. This article builds upon our past work by utilizing a dataset consisting of a range of fatigue values (from 0-7 in a scale of 10) in both low back and leg regions to predict levels of fatigue (e.g., low, medium, high). In addition, we considered intermittent trunk flexion task sequences involving static, dynamic, and sustained trunk flexion; these were performed in both symmetric and asymmetric postures. Furthermore, the inputs used for developing the models were sourced from four wearable sensors (muscle activity and inertial motion sensors), to consider the accessibility of real-world evaluations. As being assisted with an EXO while performing tasks may affect both local and global fatigue progression, the models proposed here may perform better in contrast to generic fatigue prediction models (without EXOs). Thus, the purpose of this study is to develop a model that can assist in evaluating the overall demands on BSIE users by monitoring their fatigue level as they perform routine trunk flexion tasks. Subsequently, the novelty of this study is that the developed models incorporate features while performing EXO-supported tasks, making the model specific to similar wearable assistive devices like BSIEs. Our secondary objectives were to determine the most effective grouping of fatigue levels for optimal model performance and to minimize the number of sensors for accessible evaluation. We hypothesized that features obtained from a combination of muscle activity and motion would yield the highest model performance. We also anticipated that binary grouping of back fatigue levels would yield superior model performance to tertiary grouping. The presented efforts in this study can be utilized for designing guidelines on using BSIEs based on the progression of global (perceived) fatigue levels of their users.