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

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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 proceedingOther chapter contribution

Harvard

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. 4, Lecture notes in computer science, vol. 6768, Springer, Berlin, pp. 479-488.

APA

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 Stephanidis, C. (Ed.), Universal Access in Human-Computer Interaction. (pp. 479-488). (Lecture notes in computer science). Berlin: Springer. doi: 10.1007/978-3-642-21657-2_52

Vancouver

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. Berlin: Springer. 2011. p. 479-488. (Lecture notes in computer science).

Author

Yang, Jiangang; Gao, Xuan; Wan, Baikun; Ming, Dong; Cheng, Xiaoman; Qi, Hongzhi; An, Xingwei; Chen, Long; Qiu, Shuang; Wang, Weijie / 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. ed. / Constantine Stephanidis. Vol. 4 Berlin : Springer, 2011. p. 479-488 (Lecture notes in computer science).

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Bibtex - Download

@inbook{74bbbd8d6dc34269b199b7cd81e5ae30,
title = "Intensity analysis of surface myoelectric signals from lower limbs during key gait phases by wavelets in time-frequency",
publisher = "Springer",
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",
editor = "Constantine Stephanidis",
volume = "4",
isbn = "9783642216565",
series = "Lecture notes in computer science",
pages = "479-488",
booktitle = "Universal Access in Human-Computer Interaction",

}

RIS (suitable for import to EndNote) - Download

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 -

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