Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors
文献类型：期刊论文
| 作者 | Fu, Jianting1 ; Cao, Shizhou2; Cai, Linqin2; Yang, Lechan3
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| 刊名 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
 |
| 出版日期 | 2021-11-11 |
| 卷号 | 15页码:11 |
| 关键词 | surface EMG EMG sensor finger gesture recognition convolution neural network artificial limb |
| DOI | 10.3389/fncom.2021.770692 |
| 通讯作者 | Yang, Lechan(yanglc@jit.edu.cn) |
| 英文摘要 | Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 mu V. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models. |
| 资助项目 | Surface Project of Chongqing Natural Science Fund[cstc2021jcyj-msxmX0144] |
| WOS研究方向 | Mathematical & Computational Biology ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:000733731400001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/14728]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Yang, Lechan |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China 2.Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing, Peoples R China 3.Jinling Inst Technol, Dept Soft Engn, Nanjing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fu, Jianting,Cao, Shizhou,Cai, Linqin,et al. Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2021,15:11. |
| APA | Fu, Jianting,Cao, Shizhou,Cai, Linqin,&Yang, Lechan.(2021).Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,15,11. |
| MLA | Fu, Jianting,et al."Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 15(2021):11. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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