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

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

doi:10.1007/978-3-642-21657-2_52

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

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Universal Access in Human-Computer Interaction
Subtitle of host publication	Applications and Services 6th International Conference, UAHCI 2011, Held as Part of HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings
Editors	Constantine Stephanidis
Place of Publication	Berlin
Publisher	Springer
Pages	479-488
Number of pages	10
Volume	Part 4
ISBN (Electronic)	9783642216572
ISBN (Print)	9783642216565
DOIs	https://doi.org/10.1007/978-3-642-21657-2_52
Publication status	Published - 2011
Event	6th International Conference on Universal Access in Human-Computer Interaction - Orlando, United States Duration: 9 Jul 2011 → 14 Jul 2011 http://www.hcii2011.org/

Publication series

Name	Lecture notes in computer science
Publisher	Springer
Volume	6768
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Conference on Universal Access in Human-Computer Interaction
Abbreviated title	UAHCI 2011
Country/Territory	United States
City	Orlando
Period	9/07/11 → 14/07/11
Other	Held as Part of HCI International 2011
Internet address	http://www.hcii2011.org/

Keywords

Feature extraction methods
Functional electrical stimulation
Gait phasis
Identification accuracy
Intensity analysis
Lower limb
Myoelectric signals
Other algorithms
Root Mean Square
Surface electromyogram
Time frequency
Time-resolution

Access to Document

10.1007/978-3-642-21657-2_52

Cite this

Yang, J., Gao, X., Wan, B., Ming, D., Cheng, X., Qi, H., An, X., Chen, L., Qiu, S., & Wang, W. (2011). Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency. In C. Stephanidis (Ed.), 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 (Vol. Part 4, pp. 479-488). (Lecture notes in computer science; Vol. 6768). Springer . https://doi.org/10.1007/978-3-642-21657-2_52

Yang, Jiangang ; Gao, Xuan ; Wan, Baikun et al. / Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency. 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. editor / Constantine Stephanidis. Vol. Part 4 Berlin : Springer , 2011. pp. 479-488 (Lecture notes in computer science).

@inproceedings{74bbbd8d6dc34269b199b7cd81e5ae30,

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.",

keywords = "Feature extraction methods, Functional electrical stimulation, Gait phasis, Identification accuracy, Intensity analysis, Lower limb, Myoelectric signals, Other algorithms, Root Mean Square, Surface electromyogram, Time frequency, Time-resolution",

author = "Jiangang Yang and Xuan Gao and Baikun Wan and Dong Ming and Xiaoman Cheng and Hongzhi Qi and Xingwei An and Long Chen and Shuang Qiu and Weijie Wang",

year = "2011",

doi = "10.1007/978-3-642-21657-2_52",

language = "English",

isbn = "9783642216565",

volume = "Part 4",

series = "Lecture notes in computer science",

publisher = "Springer ",

pages = "479--488",

editor = "Constantine Stephanidis",

booktitle = "Universal Access in Human-Computer Interaction",

note = "6th International Conference on Universal Access in Human-Computer Interaction , UAHCI 2011 ; Conference date: 09-07-2011 Through 14-07-2011",

url = "http://www.hcii2011.org/",

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Yang, J, Gao, X, Wan, B, Ming, D, Cheng, X, Qi, H, An, X, Chen, L, Qiu, S & Wang, W 2011, Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency. in C Stephanidis (ed.), 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. vol. Part 4, Lecture notes in computer science, vol. 6768, Springer , Berlin, pp. 479-488, 6th International Conference on Universal Access in Human-Computer Interaction , Orlando, United States, 9/07/11. https://doi.org/10.1007/978-3-642-21657-2_52

Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency. / Yang, Jiangang; Gao, Xuan; Wan, Baikun et al.
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. Part 4 Berlin: Springer , 2011. p. 479-488 (Lecture notes in computer science; Vol. 6768).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency

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

PY - 2011

Y1 - 2011

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.

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.

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

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DO - 10.1007/978-3-642-21657-2_52

M3 - Conference contribution

AN - SCOPUS:79960310063

SN - 9783642216565

VL - Part 4

T3 - Lecture notes in computer science

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BT - Universal Access in Human-Computer Interaction

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PB - Springer

CY - Berlin

T2 - 6th International Conference on Universal Access in Human-Computer Interaction

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ER -

Yang J, Gao X, Wan B, Ming D, Cheng X, Qi H et al. Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency. In Stephanidis C, editor, 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. Vol. Part 4. Berlin: Springer . 2011. p. 479-488. (Lecture notes in computer science). doi: 10.1007/978-3-642-21657-2_52

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

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