Paper Info
Title
Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency
Abstract
This paper presented a time-frequency intensity analysis feature extraction approach of lower limb sEMG (Surface Electromyogram) to identify the key gait phases during walking. The proposed feature extraction method used a filter bank of non-linearly scaled wavelets with specified time-resolution to extract time-frequency aspects of the signal.The intensity analysis algorithm was tested on sEMG data collected from ten healthy young volunteers during 30 walking circles for each. Each walking cycle was made up of four key gait phases:L-DS(Left Double Stance), L-SS(Left Single Stance), R-DS(Right Double Stance), R-SS(Right Single Stance).The identification accuracy of 7 subjects using intensity analysis reached 97%, even up to 99.42%.The others were about 95%. The algorithm obviously achieved a higher accuracy of sEMG recognition than the other algorithms such as root mean square and AR Coefficient. In the future, the feature of sEMG signal under different key gait phases may be used in the control of Functional Electrical Stimulation (FES) and other intelligent artificial limbs.
Year
DOI
Venue
2011
10.1007/978-3-642-21657-2_52
HCI (8)
Keywords
Field
DocType
right single stance,semg data,left single stance,semg recognition,semg signal,surface myoelectric signal,key gait phase,intensity analysis,lower limb semg,intensity analysis algorithm,different key gait phase
Functional electrical stimulation,Pattern recognition,Gait,Lower limb,Filter bank,Feature extraction,Speech recognition,Time–frequency analysis,Artificial intelligence,Root mean square,Mathematics,Wavelet
Conference
Volume
ISSN
Citations 
6768
0302-9743
0
PageRank 
References 
Authors
0.34
6
10
Name
Order
Citations
PageRank
Jiangang Yang100.34
Xuan Gao201.69
Baikun Wan310416.90
Dong Ming410551.47
Xiaoman Cheng501.35
Hongzhi Qi64920.61
Xingwei An72111.88
Long Chen803.04
Shuang Qiu9327.78
Weijie Wang1052.01