Paper Info
Title
Shoulder Joint Control Method For Smart Prosthetic Arm Based On Surface Emg Recognition
Abstract
This paper aims to upper limb amputees with limited motion function characteristic, based on surface myoelectric (sEMG) signal that are detectable from skin surface upper limb shoulder motion angle recognition is studied to judge for shoulder movement intention so that patients can leave the autonomy of motion control prosthetic arm. For sEMG feature extraction and pattern-recognition, two target muscle which pectorals and deltoid sEMG of subjects are collected by surface electrodes. On the basis of filtering process, sEMG root mean square (RMS) value is extracted. According to the relevance of sEMG in position of shoulder movement, sEMG characteristic values are selected as input of BP neural network and shoulder angle signal as its output. A relationship between the joint angle and sEMG signals is established. The motion control experiment is implemented with a laboratory smart prosthetic arm. The results experiment show that the neural network model can well reflect the relationship between EMG and shoulder angle.
Year
DOI
Venue
2016
10.1109/ICInfA.2016.7832014
2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA)
Keywords
Field
DocType
sEMG, Shoulder joint angle, BPNN model, Prosthetic arm control
Motion control,Shoulder joint,Upper limb,Computer science,Control theory,Filter (signal processing),Electromyography,Feature extraction,Physical medicine and rehabilitation,Artificial neural network,Deltoid curve
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Dianchun Bai126.84
Chunyu Xia200.34
Junyou Yang3111.88
Shouxian Zhang400.68
Yinlai Jiang51011.72
Hiroshi Yokoi615.79