ArticleScientific data2025
A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A Co-Located sEMG-pFMG Dataset for Hand Gesture Recognition Under Varying Arm-Position Conditions.Sensors (Basel, Switzerland) · 2026Article
- Everyday Activity Science and Engineering Table Setting Dataset.Scientific data · 2026Article
- A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures.Scientific data · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Surface electromyography (sEMG) records muscle electrical signals and reflects neuromuscular physiological behaviors. Recently, high-density sEMG (HD-sEMG), which allows non-invasive identification of motor unit action potential trains (MUAPTs) and direct access to underlining neural drive derived from the spinal cord, becomes a research hotspot. However, datasets comprising HD-sEMG signals remain limited, especially for multi-day conditions, leading to the lack of long-term investigation of motor neuron activities. This paper presents a 320-channel HD-sEMG dataset, CEMHSEY (ConsecutivE Multi-day High-density Surface ElectromyographY), recorded from forearm muscles and across 11 consecutive days. The dataset consists of two sub-datasets as: an isometric contraction dataset containing 13 subjects performing 7 grasps under 3 different contraction force levels (named GRASP) and a hand gesture dataset with 6 subjects performing 11 hand gestures (named GESTURE). The dataset was validated with the usability of force regression, hand gesture recognition, and motor unit decoding. In addition, the multi-day data provide support for developing robust human-machine interfaces as well as analyzing neuromuscular modulation.
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Registered trials
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