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基于表面肌电的意图识别方法在非理想条件下的研究进展
Alternative TitleReview of sEMG-based Motion Intent Recognition Methods in Non-ideal Conditions
李自由1,2,3; 赵新刚1,2; 张弼1,2; 丁其川4; 张道辉1,2; 韩建达1,2,5
Department机器人学研究室
Source Publication自动化学报
ISSN0254-4156
2021
Pages1-19
Contribution Rank1
Funding Organization国家自然科学基金(61773369,U1813214) ; 中国博士后科学基金项目(2019M661157)资助
Keyword肌电信号 研究进展 非理想条件 模式识别
Abstract

在基于表面肌电信号(surface electromyogram, sEMG)的意图识别研究领域,目前大多数的研究主要集中在提高肌电识别的准确性方面。然而,在实际应用中,基于sEMG识别的交互系统往往受到诸多非理想因素干扰,肌电识别的准确性被大大降低。本文主要关注在非理想条件下肌电识别的鲁棒性研究,首先详细归纳了肌电识别方法受到的非理想干扰因素(如电极偏移、个体性差异、肌肉疲劳、肢体姿态或其他综合性干扰),总结了当前研究的抗干扰方法;随后讨论了非理想干扰因素研究现状中的主要问题;最后在构建肌电数据集、探索深度学习和迁移学习,以及肌电分解研究等方面,对未来的关键技术进行了展望。

Other Abstract

In sEMG-based recognition, most studies are currently focusing on improving recognition accuracies. While in real applications, sEMG-based recognition systems are limited by many disturbances in non-ideal conditions, and recognition accuracies are worsen greatly. This paper is focusing on the robustness of sEMG-based recognition. Many disturbances in non-ideal conditions are detailed and summarized, including electrode shifts, individual differences, muscle fatigue, limb postures and others. Also, many novel methods that are proposed to remove or reduce the impact of these disturbances are summarized. Furthermore, main problems in these current studies are discussed. Finally, prospections for the future development are proposed, including building sEMG-based datasets, exploiting deep learning based and transfer learning-based recognition, and sEMG decomposition.

Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28126
Collection机器人学研究室
Corresponding Author赵新刚
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室
2.中国科学院机器人与智能制造创新研究院
3.中国科学院大学
4.东北大学机器人科学与工程学院
5.南开大学人工智能学院
Recommended Citation
GB/T 7714
李自由,赵新刚,张弼,等. 基于表面肌电的意图识别方法在非理想条件下的研究进展[J]. 自动化学报,2021:1-19.
APA 李自由,赵新刚,张弼,丁其川,张道辉,&韩建达.(2021).基于表面肌电的意图识别方法在非理想条件下的研究进展.自动化学报,1-19.
MLA 李自由,et al."基于表面肌电的意图识别方法在非理想条件下的研究进展".自动化学报 (2021):1-19.
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