Multiple features fusion system for motion recognition

Jiang Hua; Zhaojie Ju; Disi Chen; Dalin Zhou; Haoyi Zhao; Du Jiang; Gongfa Li

doi:10.1007/978-3-030-27538-9_38

Multiple features fusion system for motion recognition

Jiang Hua, Zhaojie Ju, Disi Chen, Dalin Zhou, Haoyi Zhao, Du Jiang, Gongfa Li^*

^*Corresponding author for this work

School of Computing

Wuhan University of Science and Technology

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

151 Downloads (Pure)

Abstract

Surface EMG signal is a signal source that can reflect the movement state of human muscles accurately. However, there are still problems such as low recognition rate in practical applications. It is necessary to study how they can be exploited effectively for a more accurate extraction. The paper combines two time domain features and nonlinear feature to get the feature vector for subsequent pattern recognition. The paper chooses the generalized regression neural network (GRNN) as the classifier for hand motion pattern recognition. The proposed method in this paper not only realizes the feature extraction of signals, but also ensures the high classification accuracy. The feature, RMS-SampEn-WL, obtains the highest recognition rate above 97% compared with the two time features. The new sEMG feature is effective and suitable for hand motion pattern recognition. Finally, we hope to establish a robust recognition system based on sEMG.

Original language	English
Title of host publication	Intelligent Robotics and Applications
Subtitle of host publication	12th International Conference, ICIRA 2019, Proceedings, Part IV
Editors	Haibin Yu, Jinguo Liu, Lianqing Liu, Zhaojie Ju, Yuwang Liu, Dalin Zhou
Publisher	Springer
Chapter	38
Pages	445-455
Number of pages	11
ISBN (Electronic)	978-3-030-27538-9
ISBN (Print)	978-3-030-27537-2
DOIs	https://doi.org/10.1007/978-3-030-27538-9_38
Publication status	Published - 3 Aug 2019
Event	12th International Conference on Intelligent Robotics and Applications - Shenyang, China Duration: 8 Aug 2019 → 11 Aug 2019

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11743
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	11743
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Intelligent Robotics and Applications
Abbreviated title	ICIRA 2019
Country/Territory	China
City	Shenyang
Period	8/08/19 → 11/08/19

Keywords

GRNN classifier
RMS-SampEn-WL
Surface EMG signal

Access to Document

10.1007/978-3-030-27538-9_38

ICIRA2019_348_original_v2
This is a post-peer-review, pre-copyedit version of an article published in Intelligent Robotics and Applications. ICIRA 2019. The final authenticated version is available online at: http://dx.doi.org/10.1007/10.1007/978-3-030-27538-9_38.
Accepted author manuscript (Post-print), 401 KB

Cite this

Hua, J., Ju, Z., Chen, D., Zhou, D., Zhao, H., Jiang, D., & Li, G. (2019). Multiple features fusion system for motion recognition. In H. Yu, J. Liu, L. Liu, Z. Ju, Y. Liu, & D. Zhou (Eds.), Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Proceedings, Part IV (pp. 445-455). (Lecture Notes in Computer Science; Vol. 11743), (Lecture Notes in Artificial Intelligence; Vol. 11743). Springer. https://doi.org/10.1007/978-3-030-27538-9_38

Hua, Jiang ; Ju, Zhaojie ; Chen, Disi et al. / Multiple features fusion system for motion recognition. Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Proceedings, Part IV. editor / Haibin Yu ; Jinguo Liu ; Lianqing Liu ; Zhaojie Ju ; Yuwang Liu ; Dalin Zhou. Springer, 2019. pp. 445-455 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence).

@inproceedings{322ac805f22345c5870ff5b9f0b7fcf8,

title = "Multiple features fusion system for motion recognition",

abstract = "Surface EMG signal is a signal source that can reflect the movement state of human muscles accurately. However, there are still problems such as low recognition rate in practical applications. It is necessary to study how they can be exploited effectively for a more accurate extraction. The paper combines two time domain features and nonlinear feature to get the feature vector for subsequent pattern recognition. The paper chooses the generalized regression neural network (GRNN) as the classifier for hand motion pattern recognition. The proposed method in this paper not only realizes the feature extraction of signals, but also ensures the high classification accuracy. The feature, RMS-SampEn-WL, obtains the highest recognition rate above 97% compared with the two time features. The new sEMG feature is effective and suitable for hand motion pattern recognition. Finally, we hope to establish a robust recognition system based on sEMG.",

keywords = "GRNN classifier, RMS-SampEn-WL, Surface EMG signal",

author = "Jiang Hua and Zhaojie Ju and Disi Chen and Dalin Zhou and Haoyi Zhao and Du Jiang and Gongfa Li",

year = "2019",

month = aug,

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doi = "10.1007/978-3-030-27538-9_38",

language = "English",

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series = "Lecture Notes in Computer Science",

publisher = "Springer",

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editor = "Haibin Yu and Jinguo Liu and Lianqing Liu and Zhaojie Ju and Yuwang Liu and Dalin Zhou",

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note = "12th International Conference on Intelligent Robotics and Applications, ICIRA 2019 ; Conference date: 08-08-2019 Through 11-08-2019",

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Hua, J, Ju, Z, Chen, D, Zhou, D, Zhao, H, Jiang, D & Li, G 2019, Multiple features fusion system for motion recognition. in H Yu, J Liu, L Liu, Z Ju, Y Liu & D Zhou (eds), Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Proceedings, Part IV. Lecture Notes in Computer Science, vol. 11743, Lecture Notes in Artificial Intelligence, vol. 11743, Springer, pp. 445-455, 12th International Conference on Intelligent Robotics and Applications, Shenyang, China, 8/08/19. https://doi.org/10.1007/978-3-030-27538-9_38

Multiple features fusion system for motion recognition. / Hua, Jiang; Ju, Zhaojie; Chen, Disi et al.
Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Proceedings, Part IV. ed. / Haibin Yu; Jinguo Liu; Lianqing Liu; Zhaojie Ju; Yuwang Liu; Dalin Zhou. Springer, 2019. p. 445-455 (Lecture Notes in Computer Science; Vol. 11743), (Lecture Notes in Artificial Intelligence; Vol. 11743).

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

TY - GEN

T1 - Multiple features fusion system for motion recognition

AU - Hua, Jiang

AU - Ju, Zhaojie

AU - Chen, Disi

AU - Zhou, Dalin

AU - Zhao, Haoyi

AU - Jiang, Du

AU - Li, Gongfa

PY - 2019/8/3

Y1 - 2019/8/3

N2 - Surface EMG signal is a signal source that can reflect the movement state of human muscles accurately. However, there are still problems such as low recognition rate in practical applications. It is necessary to study how they can be exploited effectively for a more accurate extraction. The paper combines two time domain features and nonlinear feature to get the feature vector for subsequent pattern recognition. The paper chooses the generalized regression neural network (GRNN) as the classifier for hand motion pattern recognition. The proposed method in this paper not only realizes the feature extraction of signals, but also ensures the high classification accuracy. The feature, RMS-SampEn-WL, obtains the highest recognition rate above 97% compared with the two time features. The new sEMG feature is effective and suitable for hand motion pattern recognition. Finally, we hope to establish a robust recognition system based on sEMG.

AB - Surface EMG signal is a signal source that can reflect the movement state of human muscles accurately. However, there are still problems such as low recognition rate in practical applications. It is necessary to study how they can be exploited effectively for a more accurate extraction. The paper combines two time domain features and nonlinear feature to get the feature vector for subsequent pattern recognition. The paper chooses the generalized regression neural network (GRNN) as the classifier for hand motion pattern recognition. The proposed method in this paper not only realizes the feature extraction of signals, but also ensures the high classification accuracy. The feature, RMS-SampEn-WL, obtains the highest recognition rate above 97% compared with the two time features. The new sEMG feature is effective and suitable for hand motion pattern recognition. Finally, we hope to establish a robust recognition system based on sEMG.

KW - GRNN classifier

KW - RMS-SampEn-WL

KW - Surface EMG signal

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DO - 10.1007/978-3-030-27538-9_38

M3 - Conference contribution

AN - SCOPUS:85070531365

SN - 978-3-030-27537-2

T3 - Lecture Notes in Computer Science

SP - 445

EP - 455

BT - Intelligent Robotics and Applications

A2 - Yu, Haibin

A2 - Liu, Jinguo

A2 - Liu, Lianqing

A2 - Ju, Zhaojie

A2 - Liu, Yuwang

A2 - Zhou, Dalin

PB - Springer

T2 - 12th International Conference on Intelligent Robotics and Applications

Y2 - 8 August 2019 through 11 August 2019

ER -

Hua J, Ju Z, Chen D, Zhou D, Zhao H, Jiang D et al. Multiple features fusion system for motion recognition. In Yu H, Liu J, Liu L, Ju Z, Liu Y, Zhou D, editors, Intelligent Robotics and Applications: 12th International Conference, ICIRA 2019, Proceedings, Part IV. Springer. 2019. p. 445-455. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-030-27538-9_38

Multiple features fusion system for motion recognition

Abstract

Publication series

Conference

Keywords

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Cite this