# FS-HGR: Few-shot Learning for Hand Gesture Recognition via ElectroMyography

arXiv: https://arxiv.org/abs/2011.06104 · HTML: https://mlsanity.com/paper/2011.06104/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 17 of 17)

- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)
- [A Methodological and Structural Review of Hand Gesture Recognition Across Diverse Data Modalities](https://mlsanity.com/paper/2408.05436/) (2024)
- [TEMGNet: Deep Transformer-based Decoding of Upperlimb sEMG for Hand Gestures Recognition](https://mlsanity.com/paper/2109.12379/) (2021)
- [EMGBench: Benchmarking Out-of-Distribution Generalization and Adaptation for Electromyography](https://mlsanity.com/paper/2410.23625/) (2024)
- [Hand Gesture Recognition Using Temporal Convolutions and Attention Mechanism](https://mlsanity.com/paper/2110.08717/) (2021)
- [ViT-HGR: Vision Transformer-based Hand Gesture Recognition from High Density Surface EMG Signals](https://mlsanity.com/paper/2201.10060/) (2022)
- [WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch](https://mlsanity.com/paper/2502.05783/) (2025)
- [Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification](https://mlsanity.com/paper/2112.15019/) (2021)
- [Fine-grained Hand Gesture Recognition in Multi-viewpoint Hand Hygiene](https://mlsanity.com/paper/2109.02917/) (2021)
- [Analyzing the Impact of Varied Window Hyper-parameters on Deep CNN for sEMG based Motion Intent Classification](https://mlsanity.com/paper/2209.05804/) (2022)
- [A Physics-Informed Low-Shot Learning For sEMG-Based Estimation of Muscle Force and Joint Kinematics](https://mlsanity.com/paper/2307.05361/) (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)
- [SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay](https://mlsanity.com/paper/2404.15305/) (2024)
- [Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning](https://mlsanity.com/paper/2404.15360/) (2024)
- [Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics](https://mlsanity.com/paper/2504.03221/) (2025)
- [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)

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their widespread applications in human-machine interfaces. DNNs have been recently used for detecting the intended hand gesture through processing of surface electromyogram (sEMG) signals. The ultimate goal of these approaches is to realize high-performance controllers for prosthetic. However, although DNNs have shown superior accuracy than conventional methods when large amounts of data are available for training, their performance substantially decreases when data are limited. Collecting large datasets for training may be feasible in research laboratories, but it is not a practical approach for real-life applications. Therefore, there is an unmet need for the design of a modern gesture detection technique that relies on minimal training data while providing high accuracy. Here we propose an innovative and novel "Few-Shot Learning" framework based on the formulation of meta-learning, referred to as the FS-HGR, to address this need. Few-shot learning is a variant of domain adaptation with the goal of inferring the required output based on just one or a few training examples. More specifically, the proposed FS-HGR quickly generalizes after seeing very few examples from each class. The proposed approach led to 85.94% classification accuracy on new repetitions with few-shot observation (5-way 5-shot), 81.29% accuracy on new subjects with few-shot observation (5-way 5-shot), and 73.36% accuracy on new gestures with few-shot observation (5-way 5-shot).