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

arXiv: https://arxiv.org/abs/1901.02442 · HTML: https://mlsanity.com/paper/1901.02442/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2019.

## Built on by (most cited first, 5 of 5)

- [Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition](https://mlsanity.com/paper/2203.12932/) (2022)
- [Long-term stable Electromyography classification using Canonical Correlation Analysis](https://mlsanity.com/paper/2301.09729/) (2023)
- [A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning](https://mlsanity.com/paper/2310.03752/) (2023)
- [Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics](https://mlsanity.com/paper/2504.03221/) (2025)
- [An Evolutionary Network Architecture Search Framework with Adaptive Multimodal Fusion for Hand Gesture Recognition](https://mlsanity.com/paper/2403.18208/) (2024)

## Abstract (arXiv)

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.