Muscle fatigue is a risk factor for developing musculoskeletal disorders during low-load repetitive tasks. The objective of this study was to assess the effect of muscle fatigue on power spectrum changes of upper limb and trunk acceleration and angular velocity during a repetitive pointing task (RPT) and a work task. Twenty-four participants equipped with 11 inertial measurement units, that include acceleration and gyroscope sensors, performed a tea bag filling work task before and immediately after a fatiguing RPT. During the RPT, the power spectrum of acceleration and angular velocity increased in the movement and in 6–12 Hz frequency bands for sensors positioned on the head, sternum, and pelvis. Alternatively, for the sensor positioned on the hand, the power spectrum of acceleration and angular velocity decreased in the movement frequency band. During the work task, following the performance of the fatiguing RPT, the power spectrum of acceleration and angular velocity increased in the movement frequency band for sensors positioned on the head, sternum, pelvis, and arm. Interestingly, for both the RPT and work task, Cohens’ d effect sizes were systematically larger for results extracted from angular velocity than acceleration. Although fatigue-related changes were task-specific between the RPT and the work task, fatigue systematically increased the power spectrum in the movement frequency band for the head, sternum, pelvis, which highlights the relevance of this indicator for assessing fatigue. Angular velocity may be more efficient to assess fatigue than acceleration. The use of low cost, wearable, and uncalibrated sensors, such as acceleration and gyroscope, in industrial settings is promising to assess muscle fatigue in workers assigned to upper limb repetitive tasks.

The myoelectric manifestation of fatigue (MMF) is predominantly assessed using median frequency and amplitude of electromyographic (EMG) signals. However, EMG has complex features so that fractals, correlation, entropy, and chaos MMF indicators were introduced to detect alteration of EMG features caused by muscle fatigue that may not be detected by linear indicators. The aim of this study was to determine the best MMF indicators. Twenty-four participants were equipped with EMG sensors on 9 shoulder muscles and performed a repetitive pointing task. They reported their rate of perceived exertion every 30 seconds and were stopped when they reached 8 or higher on the CR10 Borg scale. Partial least square regression was used to predict perceived exertion through 15 MMF indicators. In addition, the proportion of participants with a significant change between task initiation and termination was determined for each MMF indicator and muscle. The PLSR model explained 73% of the perceived exertion variance. Median frequency, mobility, spectral entropy, fuzzy entropy, and Higuchi fractal dimension had the greatest importance to predict perceived exertion and changed for 83.5% participants on average between task initiation and termination for the anterior and medial deltoids. The amplitude, activity, approximate, sample, and multiscale entropy, degree of multifractality, percent determinism and recurrent, correlation dimension, and largest Lyapunov exponent analysis MMF indicators were not efficient to assess MMF. Mobility, spectral entropy, fuzzy entropy, and Higuchi fractal dimension should be further considered to assess muscle fatigue and their combination with median frequency may further improve the assessment of muscle fatigue.