Bipolar and high-density surface EMG to investigate electrical signs of muscular fatigue

Surface electromyography (sEMG) has become an indispensable tool, extensively used across various fields such as medical diagnosis, rehabilitation, sports science, and prosthetic control. Among these applications, the study of neuromuscular adaptations related to muscle fatigue stands out due to its complexity and the intricate physiological processes underlying muscle activity. This PhD thesis aims to address this challenge by exploring the use of bipolar and high-density surface EMG (HD-EMG) to study the electrical signs of muscle fatigue across different scenarios. The primary objective is to advance our understanding of the neuromuscular system’s strategies during fatigue and to use non-invasive sEMG as a reliable method for accurately detecting and characterizing the progression of muscle fatigue. This research is structured around several key questions addressing different aspects of muscle fatigue assessment. The first part focuses on evaluating various spectral estimation techniques, as changes in amplitude and frequency content are generally observed in myoelectrical signals during fatigue. Several existing methods to estimate the spectral parameters were studied and compared to identify the optimal one providing robust indicators of fatigue. Subsequently, a significant portion of the research is dedicated to practical applications in an occupational health context. Specifically, sEMG is employed to monitor muscle fatigue in workplace settings, aiming to assess the impact of different Human-Robot Collaboration (HRC) modalities on muscle activity. The findings provide useful perspectives for developing interventions that might mitigate the risk of musculoskeletal disorders in workplace involving collaborative robots. The final part of this work demonstrates the ability of innovative technologies like HD-EMG to capture detailed spatial patterns of muscle activation, offering new ideas and quantitative measures for the study of muscle fatigue, compared to the traditional analysis performed with bipolar sEMG. Novel metrics were proposed to identify the fatigue progression and predict the endurance time during isometric contractions. Further explorations of these metrics? applicability to various muscles and different types of contractions might validate and amplify the impact of these findings, confirming their significance and utility in broader contexts. In conclusion, this thesis shows that sEMG offers novel insights into neuromuscular mechanism in presence of fatigue, improving our understanding of the neuromuscular system. The findings not only enhance methodological approaches to assess the myoelectric manifestations of fatigue, but also have visible practical implications, opening to new opportunities for injury prevention and muscle function optimization across various application fields such as sports science, clinical rehabilitation, and ergonomics.