Research of Gait Recognition Based on Human Electrostatic Signal

Mengxuan Li; Xi Chen; Shanshan Tian; Yifei Wang; Pengfei Li

doi:10.1109/IMCEC.2018.8469720

Research of Gait Recognition Based on Human Electrostatic Signal

Mengxuan Li, Xi Chen^*, Shanshan Tian, Yifei Wang, Pengfei Li

^*Corresponding author for this work

School of Mechatronical Engineering

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

5 Citations (Scopus)

Abstract

The electrostatic induction signal could be sensed when charged human body was walking, and discrepancy of walking habits such as gait frequency, stride of different individuals will be reflected in the signal. This paper proposed a method to recognize human gait by measuring electrostatic signal. A modified dynamic time warping algorithm was utilized to recognize human gait after de-noising the electrostatic signal which was acquired by a non-contact induction electrode. In order to verify the performance of this method, we collected 25 gait signal segments from 5 subjects, 5 signal segments for each subject. The result demonstrates that our method has a correct rate over 85%, and greatly shortens the operative time compared with the traditional DTW algorithm.

Original language	English
Title of host publication	Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1812-1817
Number of pages	6
ISBN (Electronic)	9781538618035
DOIs	https://doi.org/10.1109/IMCEC.2018.8469720
Publication status	Published - 20 Sept 2018
Event	2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 - Xi'an, China Duration: 25 May 2018 → 27 May 2018

Publication series

Name	Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

Conference

Conference	2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018
Country/Territory	China
City	Xi'an
Period	25/05/18 → 27/05/18

Keywords

dynamic time warping
electrostatic detection
gait recognition
gait signal

Access to Document

10.1109/IMCEC.2018.8469720

Cite this

Li, M., Chen, X., Tian, S., Wang, Y., & Li, P. (2018). Research of Gait Recognition Based on Human Electrostatic Signal. In B. Xu (Ed.), Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 (pp. 1812-1817). Article 8469720 (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMCEC.2018.8469720

Li, Mengxuan ; Chen, Xi ; Tian, Shanshan et al. / Research of Gait Recognition Based on Human Electrostatic Signal. Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1812-1817 (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018).

@inproceedings{75522a009a6347fda6f2c30974cc6b13,

title = "Research of Gait Recognition Based on Human Electrostatic Signal",

abstract = "The electrostatic induction signal could be sensed when charged human body was walking, and discrepancy of walking habits such as gait frequency, stride of different individuals will be reflected in the signal. This paper proposed a method to recognize human gait by measuring electrostatic signal. A modified dynamic time warping algorithm was utilized to recognize human gait after de-noising the electrostatic signal which was acquired by a non-contact induction electrode. In order to verify the performance of this method, we collected 25 gait signal segments from 5 subjects, 5 signal segments for each subject. The result demonstrates that our method has a correct rate over 85%, and greatly shortens the operative time compared with the traditional DTW algorithm.",

keywords = "dynamic time warping, electrostatic detection, gait recognition, gait signal",

author = "Mengxuan Li and Xi Chen and Shanshan Tian and Yifei Wang and Pengfei Li",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018 ; Conference date: 25-05-2018 Through 27-05-2018",

year = "2018",

month = sep,

day = "20",

doi = "10.1109/IMCEC.2018.8469720",

language = "English",

series = "Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1812--1817",

editor = "Bing Xu",

booktitle = "Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018",

address = "United States",

}

Li, M, Chen, X, Tian, S, Wang, Y & Li, P 2018, Research of Gait Recognition Based on Human Electrostatic Signal. in B Xu (ed.), Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018., 8469720, Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1812-1817, 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018, Xi'an, China, 25/05/18. https://doi.org/10.1109/IMCEC.2018.8469720

Research of Gait Recognition Based on Human Electrostatic Signal. / Li, Mengxuan; Chen, Xi; Tian, Shanshan et al.
Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1812-1817 8469720 (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018).

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

TY - GEN

T1 - Research of Gait Recognition Based on Human Electrostatic Signal

AU - Li, Mengxuan

AU - Chen, Xi

AU - Tian, Shanshan

AU - Wang, Yifei

AU - Li, Pengfei

PY - 2018/9/20

Y1 - 2018/9/20

N2 - The electrostatic induction signal could be sensed when charged human body was walking, and discrepancy of walking habits such as gait frequency, stride of different individuals will be reflected in the signal. This paper proposed a method to recognize human gait by measuring electrostatic signal. A modified dynamic time warping algorithm was utilized to recognize human gait after de-noising the electrostatic signal which was acquired by a non-contact induction electrode. In order to verify the performance of this method, we collected 25 gait signal segments from 5 subjects, 5 signal segments for each subject. The result demonstrates that our method has a correct rate over 85%, and greatly shortens the operative time compared with the traditional DTW algorithm.

AB - The electrostatic induction signal could be sensed when charged human body was walking, and discrepancy of walking habits such as gait frequency, stride of different individuals will be reflected in the signal. This paper proposed a method to recognize human gait by measuring electrostatic signal. A modified dynamic time warping algorithm was utilized to recognize human gait after de-noising the electrostatic signal which was acquired by a non-contact induction electrode. In order to verify the performance of this method, we collected 25 gait signal segments from 5 subjects, 5 signal segments for each subject. The result demonstrates that our method has a correct rate over 85%, and greatly shortens the operative time compared with the traditional DTW algorithm.

KW - dynamic time warping

KW - electrostatic detection

KW - gait recognition

KW - gait signal

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U2 - 10.1109/IMCEC.2018.8469720

DO - 10.1109/IMCEC.2018.8469720

M3 - Conference contribution

AN - SCOPUS:85055677780

T3 - Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

SP - 1812

EP - 1817

BT - Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018

Y2 - 25 May 2018 through 27 May 2018

ER -

Li M, Chen X, Tian S, Wang Y, Li P. Research of Gait Recognition Based on Human Electrostatic Signal. In Xu B, editor, Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1812-1817. 8469720. (Proceedings of 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2018). doi: 10.1109/IMCEC.2018.8469720

Research of Gait Recognition Based on Human Electrostatic Signal

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