# Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks

## TL;DR

This paper demonstrates that temporal convolutional networks improve the accuracy and stability of predicting hand and wrist movements from myoelectric signals, especially during movement transitions, outperforming existing methods.

## Contribution

The study introduces the use of temporal convolutional networks for myoelectric signal classification, enhancing prediction stability during movement transitions.

## Key findings

- Temporal convolutional networks outperform state-of-the-art methods in accuracy.
- They significantly improve prediction stability during movement transitions.
- The approach is validated on nine human subjects.

## Abstract

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.