ArticleFrontiers in bioengineering and biotechnology2020
Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- EMG-driven control in lower limb prostheses: a topic-based systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity.Sensors (Basel, Switzerland) · 2020Pooled it
- Electrical bioimpedance spectral markers distinguish balance ability in older adults without provoking fall risk.Journal of neuroengineering and rehabilitation · 2026Article
- An Adaptive Multi-Representation Fusion Framework for EMG Pattern Recognition in Transradial Amputees.Bioengineering (Basel, Switzerland) · 2026Article
- Mechanical signatures of left ventricular ejection fraction and mass index in a community cohort (ACE 1950).The international journal of cardiovascular imaging · 2026Article
- Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Leveraging convolutional sparse autoencoders for robust movement classification from low-density sEMG.Scientific reports · 2026Article
- A multi-scale dual-stream fusion network for high-accuracy sEMG-based gesture classification.Scientific reports · 2026Article
- Complex emotion recognition system using basic emotions via facial expression, electroencephalogram, and electrocardiogram signals: a review.Frontiers in psychology · 2026Review
- Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition.Sensors (Basel, Switzerland) · 2024Article
- Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition.Sensors (Basel, Switzerland) · 2024Article
- Context-informed incremental learning improves both the performance and resilience of myoelectric control.Journal of neuroengineering and rehabilitation · 2024Article
- A Circular, Wireless Surface-Electromyography Array.Sensors (Basel, Switzerland) · 2024Article
- A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.PloS one · 2024Article
- Big data in myoelectric control: large multi-user models enable robust zero-shot EMG-based discrete gesture recognition.Frontiers in bioengineering and biotechnology · 2024Article
- Article
- Data-Driven Quantitation of Movement Abnormality after Stroke.Bioengineering (Basel, Switzerland) · 2023Article
- Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.Journal of neuroengineering and rehabilitation · 2023Article
- Feasibility study on the application of a spiking neural network in myoelectric control systems.Frontiers in neuroscience · 2023Article
- Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics.Scientific data · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, 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. Nevertheless, 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 (Adaptive Domain Adversarial Neural Network), which significantly enhances (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.