Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
This work addresses unstable electromyographic sequence prediction for prosthetic control, representing an incremental advance in domain-specific applications.
The paper tackled the problem of unstable myoelectric signal prediction during movement transitions by using temporal convolutional networks, achieving significant performance improvements in classification accuracy and stability over state-of-the-art methods with p<0.001.
Transient muscle movements influence the temporal structure of myoelectric signal patterns, often leading to unstable prediction behavior from movement-pattern classification methods. We show that temporal convolutional network sequential models leverage the myoelectric signal's history to discover contextual temporal features that aid in correctly predicting movement intentions, especially during interclass transitions. We demonstrate myoelectric classification using temporal convolutional networks to effect 3 simultaneous hand and wrist degrees-of-freedom in an experiment involving nine human-subjects. Temporal convolutional networks yield significant $(p<0.001)$ performance improvements over other state-of-the-art methods in terms of both classification accuracy and stability.