Plantar pressure data based gait recognition by using long short-term memory network

Xiaopeng Li; Yuqing He; Xiaodian Zhang; Qian Zhao

doi:10.1007/978-3-319-97909-0_14

Plantar pressure data based gait recognition by using long short-term memory network

Xiaopeng Li, Yuqing He^*, Xiaodian Zhang, Qian Zhao

^*Corresponding author for this work

School of Optics and Photonics

Beijing Institute of Technology

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

3 Citations (Scopus)

Abstract

As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.

Original language	English
Title of host publication	Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings
Editors	Zhenan Sun, Shiguang Shan, Zhenhong Jia, Kurban Ubul, Jie Zhou, Jianjiang Feng, Zhenhua Guo, Yunhong Wang
Publisher	Springer Verlag
Pages	128-136
Number of pages	9
ISBN (Print)	9783319979083
DOIs	https://doi.org/10.1007/978-3-319-97909-0_14
Publication status	Published - 2018
Event	13th Chinese Conference on Biometric Recognition, CCBR 2018 - Urumchi, China Duration: 11 Aug 2018 → 12 Aug 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10996 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Chinese Conference on Biometric Recognition, CCBR 2018
Country/Territory	China
City	Urumchi
Period	11/08/18 → 12/08/18

Keywords

Gait recognition
LSTM
Plantar pressure data

Access to Document

10.1007/978-3-319-97909-0_14

Cite this

Li, X., He, Y., Zhang, X., & Zhao, Q. (2018). Plantar pressure data based gait recognition by using long short-term memory network. In Z. Sun, S. Shan, Z. Jia, K. Ubul, J. Zhou, J. Feng, Z. Guo, & Y. Wang (Eds.), Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings (pp. 128-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10996 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-97909-0_14

Li, Xiaopeng ; He, Yuqing ; Zhang, Xiaodian et al. / Plantar pressure data based gait recognition by using long short-term memory network. Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. editor / Zhenan Sun ; Shiguang Shan ; Zhenhong Jia ; Kurban Ubul ; Jie Zhou ; Jianjiang Feng ; Zhenhua Guo ; Yunhong Wang. Springer Verlag, 2018. pp. 128-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Plantar pressure data based gait recognition by using long short-term memory network",

abstract = "As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.",

keywords = "Gait recognition, LSTM, Plantar pressure data",

author = "Xiaopeng Li and Yuqing He and Xiaodian Zhang and Qian Zhao",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 13th Chinese Conference on Biometric Recognition, CCBR 2018 ; Conference date: 11-08-2018 Through 12-08-2018",

year = "2018",

doi = "10.1007/978-3-319-97909-0_14",

language = "English",

isbn = "9783319979083",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "Zhenan Sun and Shiguang Shan and Zhenhong Jia and Kurban Ubul and Jie Zhou and Jianjiang Feng and Zhenhua Guo and Yunhong Wang",

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Li, X, He, Y, Zhang, X & Zhao, Q 2018, Plantar pressure data based gait recognition by using long short-term memory network. in Z Sun, S Shan, Z Jia, K Ubul, J Zhou, J Feng, Z Guo & Y Wang (eds), Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10996 LNCS, Springer Verlag, pp. 128-136, 13th Chinese Conference on Biometric Recognition, CCBR 2018, Urumchi, China, 11/08/18. https://doi.org/10.1007/978-3-319-97909-0_14

Plantar pressure data based gait recognition by using long short-term memory network. / Li, Xiaopeng; He, Yuqing; Zhang, Xiaodian et al.
Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. ed. / Zhenan Sun; Shiguang Shan; Zhenhong Jia; Kurban Ubul; Jie Zhou; Jianjiang Feng; Zhenhua Guo; Yunhong Wang. Springer Verlag, 2018. p. 128-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10996 LNCS).

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

TY - GEN

T1 - Plantar pressure data based gait recognition by using long short-term memory network

AU - Li, Xiaopeng

AU - He, Yuqing

AU - Zhang, Xiaodian

AU - Zhao, Qian

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.

AB - As a kind of continuous time series, plantar pressure data contains rich contact of time information which has not been fully utilized in existing gait recognition methods. In this paper, we proposed a new gait recognition method based on plantar pressure data with a Long Short-Term Memory (LSTM) network. By normalization and dimensionality reduction, the raw pressure data was converted to feature tensor. Then we feed the LSTM network with the feature tensors and implement classification recognition. We collected data from 93 subjects of different age groups, and each subjects was collected 10 sets of pressure data. The experiment results turn out that our LSTM network can get high classification accuracy and performs better than CNN model and many traditional methods.

KW - Gait recognition

KW - LSTM

KW - Plantar pressure data

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SN - 9783319979083

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 128

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BT - Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings

A2 - Sun, Zhenan

A2 - Shan, Shiguang

A2 - Jia, Zhenhong

A2 - Ubul, Kurban

A2 - Zhou, Jie

A2 - Feng, Jianjiang

A2 - Guo, Zhenhua

A2 - Wang, Yunhong

PB - Springer Verlag

T2 - 13th Chinese Conference on Biometric Recognition, CCBR 2018

Y2 - 11 August 2018 through 12 August 2018

ER -

Li X, He Y, Zhang X, Zhao Q. Plantar pressure data based gait recognition by using long short-term memory network. In Sun Z, Shan S, Jia Z, Ubul K, Zhou J, Feng J, Guo Z, Wang Y, editors, Biometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings. Springer Verlag. 2018. p. 128-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-97909-0_14

Plantar pressure data based gait recognition by using long short-term memory network

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