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Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency

Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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  • Jiangang Yang
  • Xuan Gao
  • Baikun Wan
  • Dong Ming
  • Xiaoman Cheng
  • Hongzhi Qi
  • Xingwei An
  • Long Chen
  • Shuang Qiu
  • Weijie Wang

Research units


Original languageEnglish
Title of host publicationUniversal Access in Human-Computer Interaction
Subtitle of host publicationApplications and Services 6th International Conference, UAHCI 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings
EditorsConstantine Stephanidis
Place of PublicationBerlin
Number of pages10
VolumePart 4
ISBN (Electronic)9783642216572
ISBN (Print)9783642216565
StatePublished - 2011

Publication series

NameLecture notes in computer science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Conference on Universal Access in Human-Computer Interaction
Abbreviated titleUAHCI 2011
CountryUnited States
OtherHeld as Part of HCI International 2011
Internet address


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.

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