Longitudinal assessment of muscle function after Total Hip Arthroplasty Use of clustering to extract principal activations from EMG signals

Statistical gait analysis (SGA) is a powerful method to quantitatively assess the muscle function during locomotion. Nevertheless, the problem of dataset comparison is still very challenging when considering EMG gait signals. Aim of this study was to cope with the problem of EMG dataset comparison by extracting principal activations through the CIMAP algorithm. In particular, we analyzed and compared EMG gait data of 20 hip prosthesis patients at 3, 6 and 12 months after surgery and 20 matched control subjects. The information obtained provides a compact description of the muscle activation patters in the analyzed populations. The proposed approach can be useful to clinicians for easily comparing different populations and/or different time points evaluations from the same population (follow-up).