DECODING MUSCLE EXCITATION IN RESISTANCE TRAINING: A HIGH-DENSITY SEMG APPROACH

Resistance training (RT) is a primary stimulus for neuromuscular and structural adaptations, yet the characterization of muscle excitation during RT remains limited by methodological constraints. Traditional surface electromyography (sEMG) provides a global estimate of muscle activity but lacks the spatial resolution required to capture detailed excitation patterns within and across muscles. This doctoral thesis aimed to advance the understanding of muscle excitation in RT by integrating high-density surface electromyography (HD-sEMG), enabling the simultaneous assessment of amplitude and spatial characteristics of neuromuscular activation. The project followed a progressive methodological approach, beginning with studies using classical bipolar sEMG to investigate how variations in exercise execution influence global muscle excitation. These initial investigations were followed by a series of studies employing HD-sEMG to overcome the limitations of traditional methods. Across six experimental studies conducted in resistance-trained individuals, this thesis examined the effects of biomechanical variables, including grip configuration, lifting aids, and load manipulation, on both the magnitude and spatial distribution of muscle excitation. HD-sEMG allowed for the extraction of spatial features such as root mean square amplitude maps and centroid displacement, providing novel insights into regional activation patterns and their modulation across different exercise conditions and movement phases. The findings demonstrate that muscle excitation during RT is not uniformly distributed, but instead exhibits condition-specific spatial patterns that are not detectable using conventional sEMG. These results highlight the importance of spatially resolved analyses for interpreting neuromuscular function and refining exercise prescription. Overall, this thesis establishes HD-sEMG as a valuable non-invasive tool for investigating muscle excitation in RT and contributes to a more comprehensive understanding of neuromuscular strategies underlying resistance exercise.