# Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features

arXiv: https://arxiv.org/abs/1912.00283 · HTML: https://mlsanity.com/paper/1912.00283/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Open Access Dataset for Electromyography based Multi-code Biometric Authentication](https://mlsanity.com/paper/2201.01051/) (2022)
- [TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography](https://mlsanity.com/paper/2203.16336/) (2022)
- [Multinational Address Parsing: A Zero-Shot Evaluation](https://mlsanity.com/paper/2112.04008/) (2021)
- [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)
- [Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG](https://mlsanity.com/paper/2601.23011/) (2026)
- [On-Demand Myoelectric Control Using Wake Gestures to Eliminate False Activations During Activities of Daily Living](https://mlsanity.com/paper/2402.10050/) (2024)
- [Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition](https://mlsanity.com/paper/2412.15819/) (2024)
- [DeepFeature: LLM-Empowered Context-aware Feature Generation for Wearable Biosignals](https://mlsanity.com/paper/2512.08379/) (2025)
- [Conservative Subject Invariant EMG-based Gesture Recognition](https://mlsanity.com/paper/2607.03783/) (2026)
- [Personalising a Cross-User Surface Electromyography Encoder Under a Small Calibration Budget](https://mlsanity.com/paper/2609.20296/) (2026)

## Abstract (arXiv)

The research in myoelectric control systems primarily focuses on extracting discriminative representations from the electromyographic (EMG) signal by designing handcrafted features. Recently, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. However, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN, which significantly enhances (p=0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, the main contribution of this work is to provide the first topological data analysis of EMG-based gesture recognition for the characterisation of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. Furthermore, using convolutional network visualization techniques reveal that learned features tend to ignore the most activated channel during gesture contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.