1. 中国科学院上海高等研究院
2. 上海中研久弋科技有限公司
3. 中国人民解放军65316部队
4. 中国科学院上海微系统与信息技术研究所
纸质出版:2023
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李钊, 马斌, 李海, 等. 表面肌电信号的自适应激活特征提取算法[J]. 传感器与微系统, 2023,(8):114-118.
李钊, 马斌, 李海, 等. 表面肌电信号的自适应激活特征提取算法[J]. 传感器与微系统, 2023,(8):114-118. DOI: 10.13873/J.1000-9787(2023)08-0114-05.
为了提高表面肌电(sEMG)—外骨骼系统对多使用者多节点系统的通用性,设计了一种人体sEMG信号的自适应激活特征提取(SAFE)算法。该算法通过创建信号极值点空间(EPS)描述了信号一段时间内整体的能量分布,通过一定判别方式对短期EPS与长期EPS的比较计算获取信号激活特征分数,以便确认当前时刻的肌肉激活状态。该方法摒弃了传统的类参数固定阈值算法需要先验知识的缺陷,具备良好的自适应性和泛用性。实验结果表明:所提方法在外骨骼人机交互系统的应用中可以有效提高运动识别准确率。
An automatic feature extraction and fusion model: application to electromyogram (EMG) signal classification[J]. Anil Hazarika;Lachit Dutta;Meenakshi Boro;Mausumi Barthakur;Manabendra Bhuyan.International Journal of Multimedia Information Retrieval,2018
Characterizing the SEMG patterns with myofascial pain using a multi-scale wavelet model through machine learning approaches[J]. Yu-Ching Lin;Nan-Ying Yu;Ching-Fen Jiang;Shao-Hsia Chang.Journal of Electromyography and Kinesiology,2018
Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm.[J]. Sengur Abdulkadir;Akbulut Yaman;Guo Yanhui;Bajaj Varun.Health information science and systems,2017
Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition.[J]. Na Youngjin;Kim Jung.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society,2017
Improving the Transparency of an Exoskeleton Knee Joint Based on the Understanding of Motor Intent Using Energy Kernel Method of EMG.[J]. Chen Xing;Zeng Yan;Yin Yuehong.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society,2017
Variable Cadence Walking and Ground Adaptive Standing With a Powered Ankle Prosthesis.[J]. Shultz Amanda H;Lawson Brian E;Goldfarb Michael.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society,2016
Classification of EMG signals using PCA and FFT.[J]. Güler Nihal Fatma;Koçer Sabri.Journal of medical systems,2005
The use of surface EMG power spectral analysis in the evaluation of back muscle function.[J]. Mannion A F;Connolly B;Wood K;Dolan P.Journal of rehabilitation research and development,1997(4)
EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man.[J]. Komi P V;Tesch P.European journal of applied physiology and occupational physiology,1979(1)
基于组合能量特征的表面肌电信号手势识别算法[J]. 宋佳强;裴晓敏;赵强;刘洪海.传感器与微系统,2020(06)
基于改进能量核的下肢表面肌电信号特征提取方法[J]. 石欣;朱家庆;秦鹏杰;翟马强;田文彬.仪器仪表学报,2020(01)
人体表面肌电信号的时频域特征提取研究[J]. 任丽晔;邵宗明;徐冬蕾.长春大学学报,2019(10)
不同方式躯干运动时腰部肌肉表面肌电信号特征研究[J]. 陈岚岚;韩晓鸣;胡飞锋;张旭.杭州师范大学学报(自然科学版),2019(05)
基于Gabor小波-传递熵的脑-肌电信号同步耦合分析[J]. 张园园;邹策;陈晓玲;尹永浩;程生翠;陈迎亚;谢平.生物医学工程学杂志,2017(06)
基于sEMG振子模型的骨骼肌等长收缩力与固有特性的能量核表征方法[J]. 陈幸;殷跃红;范渊杰.科学通报,2014(07)
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