Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency. / Yang, Jiangang; Gao, Xuan; Wan, Baikun; Ming, Dong; Cheng, Xiaoman; Qi, Hongzhi; An, Xingwei; Chen, Long; Qiu, Shuang; Wang, Weijie.
Universal Access in Human-Computer Interaction: Applications and Services 6th International Conference, UAHCI 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings. ed. / Constantine Stephanidis. Vol. 4 Berlin : Springer, 2011. p. 479-488 (Lecture notes in computer science).Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution
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TY - CHAP
T1 - Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency
A1 - Yang,Jiangang
A1 - Gao,Xuan
A1 - Wan,Baikun
A1 - Ming,Dong
A1 - Cheng,Xiaoman
A1 - Qi,Hongzhi
A1 - An,Xingwei
A1 - Chen,Long
A1 - Qiu,Shuang
A1 - Wang,Weijie
AU - Yang,Jiangang
AU - Gao,Xuan
AU - Wan,Baikun
AU - Ming,Dong
AU - Cheng,Xiaoman
AU - Qi,Hongzhi
AU - An,Xingwei
AU - Chen,Long
AU - Qiu,Shuang
AU - Wang,Weijie
PB - Springer
CY - Berlin
PY - 2011/1/1
Y1 - 2011/1/1
N2 - 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. © 2011 Springer-Verlag.
AB - 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. © 2011 Springer-Verlag.
KW - Feature extraction methods
KW - Functional electrical stimulation
KW - Gait phasis
KW - Identification accuracy
KW - Intensity analysis
KW - Lower limb
KW - Myoelectric signals
KW - Other algorithms
KW - Root Mean Square
KW - Surface electromyogram
KW - Time frequency
KW - Time-resolution
UR - http://www.scopus.com/inward/record.url?partnerID=yv4JPVwI&eid=2-s2.0-79960310063&md5=1f44cba7dc3217428fa8a0892e307204
U2 - 10.1007/978-3-642-21657-2_52
DO - 10.1007/978-3-642-21657-2_52
M1 - Other chapter contribution
SN - 9783642216565
VL - 4
BT - Universal Access in Human-Computer Interaction
T2 - Universal Access in Human-Computer Interaction
A2 - Stephanidis,Constantine
ED - Stephanidis,Constantine
T3 - Lecture notes in computer science
T3 - en_GB
SP - 479
EP - 488
ER -