Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition
Abstract Surface electromyography (sEMG) signals reflect spinal motor neuron activities, and can be used as intuitive inputs for human-machine interaction (HMI) via movement intent recognition. The motor neuron potentials of far-field (wrist) and near-field (forearm) decomposed from high-density (HD...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04749-8 |
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| author | Weichao Guo Zeming Zhao Zeyu Zhou Yun Fang Yang Yu Xinjun Sheng |
| author_facet | Weichao Guo Zeming Zhao Zeyu Zhou Yun Fang Yang Yu Xinjun Sheng |
| author_sort | Weichao Guo |
| collection | DOAJ |
| description | Abstract Surface electromyography (sEMG) signals reflect spinal motor neuron activities, and can be used as intuitive inputs for human-machine interaction (HMI) via movement intent recognition. The motor neuron potentials of far-field (wrist) and near-field (forearm) decomposed from high-density (HD) sEMG prospectively provide robust neural drives for HMI, which is a challenging research hotspot. However, there are no publicly available databases that include HD sEMG signals of forearm-wrist (FW) muscles, and hand kinematics (KIN). This paper presents the HD-FW KIN dataset that comprises HD 448-channel sEMG arrays distributed on forearm and wrist with simultaneously recording of finger joint angles and finger flexion forces. This dataset contains muscle activities of 21 subjects while performing 20 hand gestures, and 9 individual or combined finger flexion under two force levels. The usabilities of HD sEMG for hand gesture recognition, finger angle and force prediction were validated. The proposed database allows a comprehensive extraction of the neural drive from forearm and wrist, providing neural interfaces for the development of advanced prosthetic hand and wrist-worn consumer electronics. |
| format | Article |
| id | doaj-art-2ffb159a76f24b6c907c76994b3eb617 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-2ffb159a76f24b6c907c76994b3eb6172025-08-20T03:41:46ZengNature PortfolioScientific Data2052-44632025-03-0112111210.1038/s41597-025-04749-8Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognitionWeichao Guo0Zeming Zhao1Zeyu Zhou2Yun Fang3Yang Yu4Xinjun Sheng5Meta Robotics Institute, 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 UniversityState Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong UniversityMeta Robotics Institute, Shanghai Jiao Tong UniversityAbstract Surface electromyography (sEMG) signals reflect spinal motor neuron activities, and can be used as intuitive inputs for human-machine interaction (HMI) via movement intent recognition. The motor neuron potentials of far-field (wrist) and near-field (forearm) decomposed from high-density (HD) sEMG prospectively provide robust neural drives for HMI, which is a challenging research hotspot. However, there are no publicly available databases that include HD sEMG signals of forearm-wrist (FW) muscles, and hand kinematics (KIN). This paper presents the HD-FW KIN dataset that comprises HD 448-channel sEMG arrays distributed on forearm and wrist with simultaneously recording of finger joint angles and finger flexion forces. This dataset contains muscle activities of 21 subjects while performing 20 hand gestures, and 9 individual or combined finger flexion under two force levels. The usabilities of HD sEMG for hand gesture recognition, finger angle and force prediction were validated. The proposed database allows a comprehensive extraction of the neural drive from forearm and wrist, providing neural interfaces for the development of advanced prosthetic hand and wrist-worn consumer electronics.https://doi.org/10.1038/s41597-025-04749-8 |
| spellingShingle | Weichao Guo Zeming Zhao Zeyu Zhou Yun Fang Yang Yu Xinjun Sheng Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition Scientific Data |
| title | Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition |
| title_full | Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition |
| title_fullStr | Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition |
| title_full_unstemmed | Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition |
| title_short | Hand kinematics, high-density sEMG comprising forearm and far-field potentials for motion intent recognition |
| title_sort | hand kinematics high density semg comprising forearm and far field potentials for motion intent recognition |
| url | https://doi.org/10.1038/s41597-025-04749-8 |
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