Construction and analysis of muscle functional network for exoskeleton robot.
10.7507/1001-5515.201803059
- Author:
Lingling CHEN
1
,
2
;
Cun ZHANG
3
;
Xiaowei SONG
3
;
Tengyu ZHANG
4
;
Xiaotian LIU
3
;
Zekun YANG
3
Author Information
1. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, P.R.China
2. Engineering Research Center of Intelligent Rehabilitation, Ministry of Education, Tianjin 300130, P.R.China.chenling@hebut.edu.cn.
3. School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, P.R.China.
4. National Research Center for Rehabilitation Technical Aids, Beijing 100176, P.R.China.
- Publication Type:Journal Article
- Keywords:
exoskeleton nursing robot;
muscle functional network;
node contraction method;
surface electromyography;
upper extremity muscles
- MeSH:
Electromyography;
Exoskeleton Device;
Humans;
Muscle, Skeletal;
physiology;
Robotics;
Upper Extremity
- From:
Journal of Biomedical Engineering
2019;36(4):565-572
- CountryChina
- Language:Chinese
-
Abstract:
Exoskeleton nursing robot is a typical human-machine co-drive system. To full play the subjective control and action orientation of human, it is necessary to comprehensively analyze exoskeleton wearer's surface electromyography (EMG) in the process of moving patients, especially identifying the spatial distribution and internal relationship of the EMG information. Aiming at the location of electrodes and internal relation between EMG channels, the complex muscle system at the upper limb was abstracted as a muscle functional network. Firstly, the correlation characteristics were analyzed among EMG channels of the upper limb using the mutual information method, so that the muscle function network was established. Secondly, by calculating the characteristic index of network node, the features of muscle function network were analyzed for different movements. Finally, the node contraction method was applied to determine the key muscle group that reflected the intention of wearer's movement, and the characteristics of muscle function network were analyzed in each stage of moving patients. Experimental results showed that the location of the myoelectric collection could be determined quickly and efficiently, and also various stages of the moving process could effectively be distinguished using the muscle functional network with the key muscle groups. This study provides new ideas and methods to decode the relationship between neural controls of upper limb and physical motion.
