# Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

arXiv: https://arxiv.org/abs/1801.07756 · HTML: https://mlsanity.com/paper/1801.07756/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2018.

## Built on by (most cited first, 30 of 47)

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- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
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- [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)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)
- [NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models](https://mlsanity.com/paper/2510.13068/) (2025)
- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)
- [Surface EMG-Based Inter-Session/Inter-Subject Gesture Recognition by Leveraging Lightweight All-ConvNet and Transfer Learning](https://mlsanity.com/paper/2305.08014/) (2023)
- [Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves](https://mlsanity.com/paper/2410.02221/) (2024)
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- [Automated Dominative Subspace Mining for Efficient Neural Architecture Search](https://mlsanity.com/paper/2210.17180/) (2022)
- [Long-term stable Electromyography classification using Canonical Correlation Analysis](https://mlsanity.com/paper/2301.09729/) (2023)
- [Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition](https://mlsanity.com/paper/2503.15528/) (2025)
- [A Montage-Agnostic Encoder for Calibration-Light Cross-User Gesture Recognition from Surface Electromyography](https://mlsanity.com/paper/2607.27565/) (2026)
- [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 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)
- [Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds](https://mlsanity.com/paper/2311.08548/) (2023)
- [Predicting Multi-Joint Kinematics of the Upper Limb from EMG Signals Across Varied Loads with a Physics-Informed Neural Network](https://mlsanity.com/paper/2312.09418/) (2023)
- [A Non-Intrusive Neural Quality Assessment Model for Surface Electromyography Signals](https://mlsanity.com/paper/2402.05482/) (2024)
- [Towards Robust and Interpretable EMG-based Hand Gesture Recognition using Deep Metric Meta Learning](https://mlsanity.com/paper/2404.15360/) (2024)
- [Spatial Adaptation Layer: Interpretable Domain Adaptation For Biosignal Sensor Array Applications](https://mlsanity.com/paper/2409.08058/) (2024)
- [Electromyography-Based Gesture Recognition: Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics](https://mlsanity.com/paper/2504.03221/) (2025)
- [Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG](https://mlsanity.com/paper/2601.23011/) (2026)
- [Agentic Neural Architecture Search](https://mlsanity.com/paper/2607.07984/) (2026)
- [One for All: An End-to-End Compact Solution for Hand Gesture Recognition](https://mlsanity.com/paper/2105.07143/) (2021)

## Abstract (arXiv)

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband. A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.