Motor Unit Action Potential Based Classification of Hand and Arm Motions

Abstract

While motion classification architectures have improved in accuracy and robustness in recent years, computationally expensive approaches and sophisticated hardware dependencies limit their real-world applicability. To overcome these challenges, we have designed a lightweight, realtime architecture for classifying motions of the arm & hand using features derived from motor unit action potentials within surface Electromyographic (sEMG) signals, rather than which provide direct interrogation of underlying muscle activation patterns. We tested the architecture on 6 motions performed dynamically across a range of muscle contraction intensities achieving median classification accuracies ranging from 91.3% to 93.3 % and an average processing time of approximately 40 ms across three different classifiers. Taken together, our findings demonstrate potential robustness of motor unit based neural interfaces for motion classification tasks.