# Detecting Fatigue during Exoskeleton-Assisted Trunk Flexion Tasks: A Machine Learning Approach
## 1 Introduction  
Wearable assistive devices such as exoskeletons have been developed to address discomfort, pain, and injuries resulting from repetitive manual tasks. The prevalence of musculoskeletal injuries from overexertion and bodily reactions has surged in recent years. For instance, in the U.S. alone, there were approximately one million reported cases of overexertion injuries in 2021-2022 [1]. Although traditional ergonomic controls such as workforce training, workstation redesign, safety protocols, and use of material handling equipment (cranes, lifts, etc.) may be beneficial in reducing injury rates, they are often expensive, require company-wide implementation, and offer less flexibility with variation in tasks [2, 3]. In most cases, despite implementing traditional ergonomic controls, workers continue to face escalating demands driven by ever-increasing consumer needs [4]. In response to these challenges, wearable assistive devices, such as Exoskeletons (EXOs), have emerged as promising solutions to augment human capabilities and mitigate the risk of injury. EXOs are typically categorized according to the body region they support, i.e., the upper body (e.g., arm/shoulder/back) or lower body (knee/ankle) [5, 6]. Of all body regions, the lumbar region of the lower back is particularly susceptible to injury, with the highest reported injury rate of approximately 17% [7]. Back-Support Industrial Exoskeletons (BSIEs) offer support to the wearer's torso during tasks requiring trunk flexion, effectively reducing the strain on lower back muscles and potentially lowering the riskof injury in this vulnerable area [8; 9; 10]. Studies have demonstrated significant reductions in lower back muscle activity when using BSIEs during static posture maintenance tasks involving trunk bending [9; 11; 12] and dynamic lifting tasks [13; 14; 15; 16; 17; 18]. However, evaluations conducted in field environments have yielded mixed results regarding the effects of BSIEs on the human body, as outlined in our previous research [6]. Fatigue, often stemming from repetitive muscle activation, renders workers more susceptible to injury, as it compromises the neuromusculoskeletal systems [19]. When the demands placed on the body exceed its capacity to generate essential forces, it can result in failure, diminished work quality, performance errors, and increased injury risk [20]. Consequently, estimating fatigue levels during task performance becomes crucial for designing safer and more efficient work environments. Traditionally, fatigue has been assessed subjectively using scales [21], and objectively by monitoring changes in muscle force generation capacity. It is defined as \"the inability of muscles to sustain force generation over time\" in the [22]. Engaging in activities while fatigued can lead to detrimental effects, such as impaired balance and reduced control over body movements, thereby escalating the risk of falls [23; 24]. While Back-Support Industrial Exoskeletons (BSIEs) may offer benefits in reducing the rate of muscle fatigue, they might also increase users' susceptibility to falls [25], particularly with additional weight (2.2-4.5 kg), and the assistive torque provided could impact the wearer's stability [23]. These concerns are exacerbated during dynamic tasks with increased inertial forces and in awkward or asymmetric postures [18; 26; 27; 28]. To ensure the safety and efficiency of EXOs, it is crucial to integrate advanced technologies through which EXOs can provide optimum assistance to wearers, improving their wearer's experience and reducing the risk of injuries. Machine learning algorithms, particularly classification methods, have emerged as powerful tools across domains of manufacturing [29], construction [30], and healthcare [31] to analyze complex data and for making predictions. In healthcare, these algorithms offer advantages by improving disease diagnosis, assessing risks, and tailoring treatment plans based on learned data patterns [32].