ArticleScientific data2022
Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles.
Article in Scientific data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 41 citations in OpenAlex.
- Optimization and Device-Level Validation of the i-Phagia Surface Electromyography Submental Sensor Patch for Swallowing Monitoring: A Randomized Crossover Design Study.Journal of speech, language, and hearing research : JSLHR · 2026Trial
- From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.Scientific reports · 2026Article
- Live magnetomyography and robotic-hand proxy control using a wearable triaxial optically pumped magnetometer.Scientific reports · 2026Article
- Review
- Artificial-Intelligence-Driven Electromyography Adaptation for Elderly Assistance at Physiological, Functional, and Behavioral Levels.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Cloud-Based Personalized sEMG Classification Using Lightweight CNNs for Long-Term Haptic Communication in Deaf-Blind Individuals.Bioengineering (Basel, Switzerland) · 2025Article
- A parallel and efficient transformer deep learning network for continuous estimation of hand kinematics from electromyographic signals.Scientific reports · 2025Article
- A database of multimodal myography and hand kinematics during realistic daily life activities.Scientific data · 2025Article
- HANDSON Hand: Strategies and Approaches for Competitive Success at CYBATHLON 2024.Bioengineering (Basel, Switzerland) · 2025Article
- Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System.Bioengineering (Basel, Switzerland) · 2024Article
- Efficient strategies for finger movement classification using surface electromyogram signals.Frontiers in neuroscience · 2023Article
- Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles.Scientific data · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Surface electromyography (sEMG) is commonly used to observe the motor neuronal activity within muscle fibers. However, decoding dexterous body movements from sEMG signals is still quite challenging. In this paper, we present a high-density sEMG (HD-sEMG) signal database that comprises simultaneously recorded sEMG signals of intrinsic and extrinsic hand muscles. Specifically, twenty able-bodied participants performed 12 finger movements under two paces and three arm postures. HD-sEMG signals were recorded with a 64-channel high-density grid placed on the back of hand and an 8-channel armband around the forearm. Also, a data-glove was used to record the finger joint angles. Synchronisation and reproducibility of the data collection from the HD-sEMG and glove sensors were ensured. The collected data samples were further employed for automated recognition of dexterous finger movements. The introduced dataset offers a new perspective to study the synergy between the intrinsic and extrinsic hand muscles during dynamic finger movements. As this dataset was collected from multiple participants, it also provides a resource for exploring generalized models for finger movement decoding.
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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.