Paper Info
Title
Intelligent wearable rehabilitation robot control system based on mobile communication network
Abstract
A large number of disabled people are caused by major diseases and accidents. Because disabled patients cannot exercise freely on their own, in order to prevent muscle atrophy, the most effective treatment is to exercise the patients’ limbs. Therefore, it is necessary to choose a more suitable rehabilitation training method to replace the traditional artificial rehabilitation training method, and the use of intelligent wearable rehabilitation robots to treat hemiplegia patients is particularly important for the rehabilitation treatment of hemiplegia patients. Rehabilitation robots can promote the development of medical technology and medical equipment, and have important practical significance for patients to overcome disease and recover health and build a harmonious family. This paper mainly studies the EMG research of the control system of intelligent wearable rehabilitation robot based on mobile communication network. This paper studies the way of stimulating signals and the processing of signal feedback in the process of electromyographic stimulation of rehabilitation robots to improve the rehabilitation effect of robots after stimulation; explores the problem of using biofeedback and fuzzy control rules to control patients for rehabilitation training, which urges patients actively participate in rehabilitation treatment, effectively guide the recovery of patients’ self-consciousness, and build a variety of training modes for rehabilitation robots to enhance the robot’s ability to adapt to different groups of people. In the experiments of this paper, the time window for data processing is 256ms, and the delay is also 256ms. It can be seen that the original SEMG signal of the control signal has some delay compared with the filtered SEMG signal.
Year
DOI
Venue
2020
10.1016/j.comcom.2020.01.054
Computer Communications
Keywords
Field
DocType
Mobile communication network,Smart wearable,Rehabilitation robot,Control system
Rehabilitation,Health technology,Computer science,Wearable computer,Real-time computing,Medical equipment,Control system,Fuzzy control system,Physical medicine and rehabilitation,Robot,Biofeedback
Journal
Volume
ISSN
Citations 
153
0140-3664
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Fengmei Gao100.68
Linhong Wang202.03
Tao Lin300.34