# Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism

arXiv: https://arxiv.org/abs/2110.08717 · HTML: https://mlsanity.com/paper/2110.08717/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 17 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2011.06104/) (2020)

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

- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)
- [SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network](https://mlsanity.com/paper/2405.14398/) (2024)
- [Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2210.15119/) (2022)
- [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)
- [From Unimodal to Multimodal: improving sEMG-Based Pattern Recognition via deep generative models](https://mlsanity.com/paper/2308.04091/) (2023)

## Abstract (arXiv)

Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN models have shown promising results with respect to other algorithms for decoding muscle electrical activity, especially for recognition of hand gestures. Such data-driven models, however, have been challenged by their need for a large number of trainable parameters and their structural complexity. Here we propose the novel Temporal Convolutions-based Hand Gesture Recognition architecture (TC-HGR) to reduce this computational burden. With this approach, we classified 17 hand gestures via surface Electromyogram (sEMG) signals by the adoption of attention mechanisms and temporal convolutions. The proposed method led to 81.65% and 80.72% classification accuracy for window sizes of 300ms and 200ms, respectively. The number of parameters to train the proposed TC-HGR architecture is 11.9 times less than that of its state-of-the-art counterpart.