# ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals

arXiv: https://arxiv.org/abs/2201.10060 · HTML: https://mlsanity.com/paper/2201.10060/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)
- [Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks](https://mlsanity.com/paper/1901.06958/) (2019)

## Built on by (most cited first, 6 of 6)

- [Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System](https://mlsanity.com/paper/2508.12473/) (2025)
- [Deep learning based Hand gesture recognition system and design of a Human-Machine Interface](https://mlsanity.com/paper/2207.03112/) (2022)
- [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)
- [HYDRA-HGR: A Hybrid Transformer-based Architecture for Fusion of Macroscopic and Microscopic Neural Drive Information](https://mlsanity.com/paper/2211.02619/) (2022)
- [Transformer-based Hand Gesture Recognition via High-Density EMG Signals: From Instantaneous Recognition to Fusion of Motor Unit Spike Trains](https://mlsanity.com/paper/2212.00743/) (2022)
- [Masked Autoencoder with Swin Transformer Network for Mitigating Electrode Shift in HD-EMG-based Gesture Recognition](https://mlsanity.com/paper/2410.17261/) (2024)

## Abstract (arXiv)

Recently, there has been a surge of significant interest on application of Deep Learning (DL) models to autonomously perform hand gesture recognition using surface Electromyogram (sEMG) signals. DL models are, however, mainly designed to be applied on sparse sEMG signals. Furthermore, due to their complex structure, typically, we are faced with memory constraints; require large training times and a large number of training samples, and; there is the need to resort to data augmentation and/or transfer learning. In this paper, for the first time (to the best of our knowledge), we investigate and design a Vision Transformer (ViT) based architecture to perform hand gesture recognition from High Density (HD-sEMG) signals. Intuitively speaking, we capitalize on the recent breakthrough role of the transformer architecture in tackling different complex problems together with its potential for employing more input parallelization via its attention mechanism. The proposed Vision Transformer-based Hand Gesture Recognition (ViT-HGR) framework can overcome the aforementioned training time problems and can accurately classify a large number of hand gestures from scratch without any need for data augmentation and/or transfer learning. The efficiency of the proposed ViT-HGR framework is evaluated using a recently-released HD-sEMG dataset consisting of 65 isometric hand gestures. Our experiments with 64-sample (31.25 ms) window size yield average test accuracy of 84.62 +/- 3.07%, where only 78, 210 number of parameters is utilized. The compact structure of the proposed ViT-based ViT-HGR framework (i.e., having significantly reduced number of trainable parameters) shows great potentials for its practical application for prosthetic control.