ArticleSensors (Basel, Switzerland)2024
Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Reusable and Soft Self-Adhesive Epidermal Electrodes for Human Skin Enabled by Functional Additives.Micromachines · 2026Article
- From Signal Stacking to Dynamic Coupling: A Critical Review of Wearable EEG-EMG Fusion Brain-Computer Interfaces for Stroke Rehabilitation.Micromachines · 2026Review
- EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.Scientific reports · 2026Article
- A multi-scale dual-stream fusion network for high-accuracy sEMG-based gesture classification.Scientific reports · 2026Article
- Flexible and Wearable Tactile Sensors for Intelligent Interfaces.Materials (Basel, Switzerland) · 2025Review
- MOT: A Low-Latency, Multichannel Wireless Surface Electromyography Acquisition System Based on the AD8232 Front-End.Sensors (Basel, Switzerland) · 2025Article
- Gesture Recognition Framework for Teleoperation of Infrared (IR) Consumer Devices Using a Novel pFMG Soft Armband.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging machine learning algorithms. The research entails rigorous data preprocessing to extract features and labels from raw EMG data. Following partitioning into training and testing sets, four traditional machine learning models are scrutinized for their efficacy in classifying finger movements across seven distinct gestures. The analysis includes meticulous parameter optimization and five-fold cross-validation to evaluate model performance. Among the models assessed, the Random Forest emerges as the top performer, consistently delivering superior precision, recall, and F1-score values across gesture classes, with ROC-AUC scores surpassing 99%. These findings underscore the Random Forest model as the optimal classifier for our EMG dataset, promising significant advancements in healthcare rehabilitation engineering and enhancing human-computer interaction technologies.
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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.