A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures
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...
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| Format: | Article |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05733-y |
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| author | Shutian Yang Chen Chen Dongxuan Li Xiangyang Zhu |
| author_facet | Shutian Yang Chen Chen Dongxuan Li Xiangyang Zhu |
| author_sort | Shutian Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa0f26f820434f03aa6987eaa35eb4db |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-fa0f26f820434f03aa6987eaa35eb4db2025-08-20T04:01:47ZengNature PortfolioScientific Data2052-44632025-08-0112111010.1038/s41597-025-05733-yA Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 GesturesShutian Yang0Chen Chen1Dongxuan Li2Xiangyang Zhu3State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong UniversityState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong UniversityState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong UniversityState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong UniversityAbstract 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.https://doi.org/10.1038/s41597-025-05733-y |
| spellingShingle | Shutian Yang Chen Chen Dongxuan Li Xiangyang Zhu A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures Scientific Data |
| title | A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures |
| title_full | A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures |
| title_fullStr | A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures |
| title_full_unstemmed | A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures |
| title_short | A Consecutive Multi-Day High-Density Surface Electromyography Dataset Comprising 7 Grasps and 11 Gestures |
| title_sort | consecutive multi day high density surface electromyography dataset comprising 7 grasps and 11 gestures |
| url | https://doi.org/10.1038/s41597-025-05733-y |
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