# A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning

arXiv: https://arxiv.org/abs/2310.03752 · HTML: https://mlsanity.com/paper/2310.03752/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 69 references that are in the map.
- First posted on arXiv 23 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning](https://mlsanity.com/paper/1801.07756/) (2018)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals](https://mlsanity.com/paper/2201.10060/) (2022)
- [Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks](https://mlsanity.com/paper/1901.02442/) (2019)
- [Deep learning approach to control of prosthetic hands with electromyography signals](https://mlsanity.com/paper/1909.09910/) (2019)
- [Performance Optimization of Surface Electromyography (sEMG) based Biometric Sensing System for both Verification and Identification](https://mlsanity.com/paper/2103.06015/) (2021)
- [From Unstable Contacts to Stable Control: A Deep Learning Paradigm for HD-sEMG in Neurorobotics](https://mlsanity.com/paper/2309.11086/) (2023)

## Built on by (most cited first, 1 of 1)

- [Estimation of embedding vectors in high dimensions](https://mlsanity.com/paper/2312.07802/) (2023)

## Abstract (arXiv)

Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamics of biosignals from the peripheral nervous system, such as surface electromyography (sEMG). These interfaces have a range of applications, including the control of extended reality, agile prosthetics, and exoskeletons. However, the natural variability of sEMG among individuals has led researchers to focus on subject-specific solutions. Deep learning methods, which often have complex structures, are particularly data-hungry and can be time-consuming to train, making them less practical for subject-specific applications. In this paper, we propose and develop a generalizable, sequential decoder of transient high-density sEMG (HD-sEMG) that achieves 73% average accuracy on 65 gestures for partially-observed subjects through subject-embedded transfer learning, leveraging pre-knowledge of HGR acquired during pre-training. The use of transient HD-sEMG before gesture stabilization allows us to predict gestures with the ultimate goal of counterbalancing system control delays. The results show that the proposed generalized models significantly outperform subject-specific approaches, especially when the training data is limited, and there is a significant number of gesture classes. By building on pre-knowledge and incorporating a multiplicative subject-embedded structure, our method comparatively achieves more than 13% average accuracy across partially observed subjects with minimal data availability. This work highlights the potential of HD-sEMG and demonstrates the benefits of modeling common patterns across users to reduce the need for large amounts of data for new users, enhancing practicality.