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 approach was implemented that enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the signatures, are highly robust. Additionally, we proposed a method for visualising each individual muscle activation signature, which has several clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.
bioRxiv Subject Collection: Neuroscience