ABSTRACT

Surface electromyography (sEMG) have been used to monitor muscle activity and predict fatigue in the workplaces. However, objectively measuring fatigue is challenging in complex work with unpredictable work cycles, where sEMG may be influenced by the dynamically changing posture demands. The sEMG is affected by various variables and substantial change in mean power frequencies (MPF), decline over 8-9%, is primarily considered as fatigue. These MPF thresholds have been frequently used and there were limited efforts to test their appropriateness in determining musculoskeletal fatigue in live workplaces. In addition, the techniques that consider both muscular and postural measurements that incorporate dynamic posture changes observed in complex work have not yet been explored. The overall objective of this work is to leverage both postural and muscular cues to identify the musculoskeletal fatigue. The work was completed in two studies.

The first study aimed to (1) predict subjective fatigue using objective measurements in non-repetitive tasks, (2) determine whether the musculoskeletal fatigue thresholds in non-repetitive tasks differed from the previously reported threshold, and (3) utilize the empirically calculated thresholds to test their appropriateness in determining musculoskeletal fatigue in live surgical workplaces. The findings showed that the thresholds in dynamic non-repetitive tasks, like surgery, are different than the previously reported 8% threshold. In addition, implementing muscle specific thresholds increased the likelihood of more accurately reporting subjective fatigue. The second study aimed to develop a multi-modal fatigue index to detect musculoskeletal fatigue. A controlled laboratory study was performed to simulate the non-repetitive physical demands at different postures. The composite fatigue index (CFI) function was developed using the time-synced integration of both muscular signals (measured with sEMG sensors) and postural signals (measured with Inertial Measurement Unit (IMU) sensors). The variables from sEMG (amplitude, frequency, and number of muscles showing signs of fatigue) and IMU (prevalence of static/demanding postures and number of shoulders in static/demanding posture) sensors were integrated to generate the CFI function. The single value fatigue index is obtained using the resultant CFI function, which incorporates both muscular and postural variables, to quantify the muscular fatigue in dynamic non-repetitive tasks. The findings suggested that the propagation of musculoskeletal fatigue can be detected using the multi-modal composite fatigue index. The resultant CFI function was then applied to surgery tasks to differentiate the differences between fatigued and non-fatigued groups. The findings showed that the multi-modal fatigue assessment techniques can be utilized to incorporate the muscular and postural measurements to identify fatigue in complex tasks beyond single-modality assessment approaches.