Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation

Weixi Zhu; Dong Ming; Baikun Wan; Xiaoman Cheng; Hongzhi Qi; Yuanyuan Chen; Rui Xu; Weijie Wang

doi:10.1007/978-3-642-21657-2_53

Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation

Weixi Zhu, Dong Ming, Baikun Wan, Xiaoman Cheng, Hongzhi Qi, Yuanyuan Chen, Rui Xu, Weijie Wang

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

Abstract

This paper proposed Fuzzy clustering of C means and K means methods to extract the lateral features of lower limbs movement from handle reaction vector (HRV )data. With C-means clustering, the SVM recognition rate of lateral features was usually above 90% while, with K-means clustering, the recognition rate was close to 85%. The best recognition rate was even reaching up to 97% for some individual subject. Then the samples from all subjects were processed together with the cross-validation. Our experimental results showed that the HRV signal could be used with fuzzy clustering and support vector machine to effectively classify the lateral features of lower limbs movement. It may provide a new choice for FES control signal. The optimizing of the algorism parameters can be introduced to get better control in the future.

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
Pages	489-498
Number of pages	10
Volume	4
ISBN (Electronic)	9783642216572
DOIs	https://doi.org/10.1007/978-3-642-21657-2_53
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

C-means clustering
Control signal
Cross validation
Handle reaction vectors
K-means clustering
Lower limb
Recognition rates
Fuzzy clustering
Support vector machines
Walking aids

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-21657-2_53

Cite this

Zhu, W., Ming, D., Wan, B., Cheng, X., Qi, H., Chen, Y., Xu, R., & Wang, W. (2011). Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation. 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, pp. 489-498). (Lecture notes in computer science; Vol. 6768). https://doi.org/10.1007/978-3-642-21657-2_53

Zhu, Weixi ; Ming, Dong ; Wan, Baikun et al. / Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation. 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. 4 2011. pp. 489-498 (Lecture notes in computer science).

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title = "Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation",

abstract = "This paper proposed Fuzzy clustering of C means and K means methods to extract the lateral features of lower limbs movement from handle reaction vector (HRV )data. With C-means clustering, the SVM recognition rate of lateral features was usually above 90% while, with K-means clustering, the recognition rate was close to 85%. The best recognition rate was even reaching up to 97% for some individual subject. Then the samples from all subjects were processed together with the cross-validation. Our experimental results showed that the HRV signal could be used with fuzzy clustering and support vector machine to effectively classify the lateral features of lower limbs movement. It may provide a new choice for FES control signal. The optimizing of the algorism parameters can be introduced to get better control in the future.",

keywords = "C-means clustering, Control signal, Cross validation, Handle reaction vectors, K-means clustering, Lower limb, Recognition rates, Fuzzy clustering, Support vector machines, Walking aids",

author = "Weixi Zhu and Dong Ming and Baikun Wan and Xiaoman Cheng and Hongzhi Qi and Yuanyuan Chen and Rui Xu and Weijie Wang",

year = "2011",

doi = "10.1007/978-3-642-21657-2_53",

language = "English",

isbn = "9783642216565",

volume = "4",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "489--498",

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",

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}

Zhu, W, Ming, D, Wan, B, Cheng, X, Qi, H, Chen, Y, Xu, R & Wang, W 2011, Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation. 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, pp. 489-498, 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_53

Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation. / Zhu, Weixi; Ming, Dong; 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. 4 2011. p. 489-498 (Lecture notes in computer science; Vol. 6768).

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

TY - GEN

T1 - Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation

AU - Zhu, Weixi

AU - Ming, Dong

AU - Wan, Baikun

AU - Cheng, Xiaoman

AU - Qi, Hongzhi

AU - Chen, Yuanyuan

AU - Xu, Rui

AU - Wang, Weijie

PY - 2011

Y1 - 2011

N2 - This paper proposed Fuzzy clustering of C means and K means methods to extract the lateral features of lower limbs movement from handle reaction vector (HRV )data. With C-means clustering, the SVM recognition rate of lateral features was usually above 90% while, with K-means clustering, the recognition rate was close to 85%. The best recognition rate was even reaching up to 97% for some individual subject. Then the samples from all subjects were processed together with the cross-validation. Our experimental results showed that the HRV signal could be used with fuzzy clustering and support vector machine to effectively classify the lateral features of lower limbs movement. It may provide a new choice for FES control signal. The optimizing of the algorism parameters can be introduced to get better control in the future.

AB - This paper proposed Fuzzy clustering of C means and K means methods to extract the lateral features of lower limbs movement from handle reaction vector (HRV )data. With C-means clustering, the SVM recognition rate of lateral features was usually above 90% while, with K-means clustering, the recognition rate was close to 85%. The best recognition rate was even reaching up to 97% for some individual subject. Then the samples from all subjects were processed together with the cross-validation. Our experimental results showed that the HRV signal could be used with fuzzy clustering and support vector machine to effectively classify the lateral features of lower limbs movement. It may provide a new choice for FES control signal. The optimizing of the algorism parameters can be introduced to get better control in the future.

KW - C-means clustering

KW - Control signal

KW - Cross validation

KW - Handle reaction vectors

KW - K-means clustering

KW - Lower limb

KW - Recognition rates

KW - Fuzzy clustering

KW - Support vector machines

KW - Walking aids

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U2 - 10.1007/978-3-642-21657-2_53

DO - 10.1007/978-3-642-21657-2_53

M3 - Conference contribution

AN - SCOPUS:79960298133

SN - 9783642216565

VL - 4

T3 - Lecture notes in computer science

SP - 489

EP - 498

BT - Universal Access in Human-Computer Interaction

A2 - Stephanidis, Constantine

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

Y2 - 9 July 2011 through 14 July 2011

ER -

Zhu W, Ming D, Wan B, Cheng X, Qi H, Chen Y et al. Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation. 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. 4. 2011. p. 489-498. (Lecture notes in computer science). doi: 10.1007/978-3-642-21657-2_53

Handle reaction vector analysis with fuzzy clustering and support vector machine during FES-assisted walking rehabilitation

Abstract

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Conference

Keywords

UN SDGs

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