Clusterization of multi-channel electromyograms into muscle-specific activations to drive a subject-specific musculoskeletal model: towards fast and accurate clinical decision-making

Current clinical decision-making is based on rapid and subjective functional tests such as 10 m walking. Moreover, greater accuracy can be achieved at the expense of rapidity and costs. In biomechanical laboratories, advanced technologies and musculoskeletal modeling can quantitatively describe the biomechanical reasons underlying gait disorders. Our work aims to blend clinical rapidity and biomechanical accuracy through multi-channel (MC) electromyography (EMG) clustering and real-time neuro-musculoskeletal (NMS) modeling techniques integrated into a sensorized wearable garment that is quick to set up. Here we present a unique pipeline that goes from MC EMG signals to ankle torque estimation following two steps: (1) non-negative matrix factorization (NNMF)-based EMG clustering for the extraction of muscle-specific activations and (2) subject-specific EMG-driven NMS modeling. The results show the potential of NNMF as an electrode clustering tool, as well as the ability to predict joint torque during movements that were not used for the EMG clustering.