Paper Info
Title
Towards Active Muscle Pattern Analysis for Dynamic Hand Motions via sEMG.
Abstract
Surface Electromyographys (sEMG) as a widespread human-computer interaction method can reflect the activity of human muscles. When the human forearm finishes different hand motions, there will be strong sEMG signals in different regions of the skin surface. This paper investigates the mapping relationship between sEMG signal patterns and the dynamic hand motions. Four different hand motions are studied based on the extracted signal with mean absolute value (MAV) features and the shape-preserving piecewise cubic interpolation method. In the experiments, a 16-channel electrode sleeve is used to collect 9-subject EMG signals. According to the distribution of electrodes in the forearm, the forearm surface is divided into 8 different muscle regions. The preliminary experimental results show that different hand motions can cause different distribution of sEMG signals in different regions. It confirms that different subjects show similar patterns for the same motions. The experimental results can be applied as new sEMG features with a higher computational speed.
Year
DOI
Venue
2018
10.1007/978-3-319-97982-3_31
ADVANCES IN COMPUTATIONAL INTELLIGENCE SYSTEMS (UKCI)
Keywords
Field
DocType
sEMG,MAV,Shape-preserving piecewise cubic interpolation,Local maximum,Muscle regions
Spline interpolation,Pattern recognition,Pattern analysis,Forearm,Forearm surface,Artificial intelligence,Mean absolute value,Piecewise,Mathematics
Conference
Volume
ISSN
Citations 
840
2194-5357
0
PageRank 
References 
Authors
0.34
5
6
Name
Order
Citations
PageRank
Jiahan Li191.82
Yinfeng Fang24910.00
Yongan Huang3186.45
Gongfa Li423943.45
Zhaojie Ju528448.23
Honghai Liu61974178.69