Reconstruction of incomplete surface electromyography based on an adversarial autoencoder network
文献类型：期刊论文
| 作者 | Zou, Yongxiang ; Cheng, Long1; Han, Lijun
|
| 刊名 | BIOMEDICAL SIGNAL PROCESSING AND CONTROL
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 86页码:11 |
| 关键词 | Adversarial autoencoder Missing sEMG Signal reconstruction Self-mask Multi-view discriminator |
| ISSN号 | 1746-8094 |
| DOI | 10.1016/j.bspc.2023.105084 |
| 通讯作者 | Cheng, Long(long.cheng@ia.ac.cn) |
| 英文摘要 | Surface electromyography (sEMG) signals are often incomplete due to interferences during data measurement, which can degrade sEMG-based applications. To address this issue, this paper proposes a novel adversarial autoencoder model, called the SGMD-AAE, which includes a self-mask generator and a multi-view discrimi-nator. The generator's binary mask is replaced with a self-mask mechanism, and an adversarial loss is added to promote the reconstruction performance. The multi-view discriminator extracts and fuses deep features of sEMG from time and frequency domains to enhance the generator's reconstruction ability. The SGMD-AAE model is evaluated on the benchmark NinaPro DB2 database, and the experimental results show that it significantly outperforms incomplete sEMG signals, reducing NRMSE by 88.04% and increasing PSNR by 116.21%. The proposed model also achieves high recognition accuracy for hand gesture recognition even in extreme cases where 90% of the sEMG signals are missing, with an average accuracy exceeding 84%. The effectiveness of the SGMD-AAE model is further verified on a self-collected dataset, demonstrating similar recognition results. |
| WOS关键词 | ALGORITHMS ; IMPUTATION |
| 资助项目 | National Key Research amp; Development Program, China[2022YFB4703204] ; National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[U1913209] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001021230500001 |
| 出版者 | ELSEVIER SCI LTD |
| 资助机构 | National Key Research amp; Development Program, China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53642]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Cheng, Long |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zou, Yongxiang,Cheng, Long,Han, Lijun. Reconstruction of incomplete surface electromyography based on an adversarial autoencoder network[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2023,86:11. |
| APA | Zou, Yongxiang,Cheng, Long,&Han, Lijun.(2023).Reconstruction of incomplete surface electromyography based on an adversarial autoencoder network.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,86,11. |
| MLA | Zou, Yongxiang,et al."Reconstruction of incomplete surface electromyography based on an adversarial autoencoder network".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 86(2023):11. |
入库方式： OAI收割
来源：自动化研究所
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