Revealing the unique features of each individual’s muscle activation signatures
AbstractThere is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear Support Vector Machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision making by the machine learning classification model, a Layer-wise Relevance Propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each i…
Revealing the unique features of each individual's muscle activation signatures
International audience; There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance …