Segmentation of human body movement using inertial measurement unit

Takashi Aoki; Gentiane Venture; Dana Kulić

doi:10.1109/SMC.2013.205

Segmentation of human body movement using inertial measurement unit

Takashi Aoki, Gentiane Venture, Dana Kulić

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

12 Citations (Scopus)

Abstract

This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.

Original language	English
Title of host publication	Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1181-1186
Number of pages	6
ISBN (Print)	9780769551548
DOIs	https://doi.org/10.1109/SMC.2013.205
Publication status	Published - 1 Dec 2013
Externally published	Yes
Event	IEEE International Conference on Systems, Man and Cybernetics 2013 - Manchester, United Kingdom Duration: 13 Oct 2013 → 16 Oct 2013 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6689802 (IEEE Conference Proceedings)

Publication series

Name	Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Conference

Conference	IEEE International Conference on Systems, Man and Cybernetics 2013
Abbreviated title	SMC 2013
Country/Territory	United Kingdom
City	Manchester
Period	13/10/13 → 16/10/13
Internet address	http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6689802 (IEEE Conference Proceedings)

Keywords

Arm motion
HMM
Inertial measurement unit
Recognition
Segmentation
Wii remote

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10.1109/SMC.2013.205

Cite this

Aoki, T., Venture, G., & Kulić, D. (2013). Segmentation of human body movement using inertial measurement unit. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 (pp. 1181-1186). Article 6721958 (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SMC.2013.205

Aoki, Takashi ; Venture, Gentiane ; Kulić, Dana. / Segmentation of human body movement using inertial measurement unit. Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1181-1186 (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013).

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title = "Segmentation of human body movement using inertial measurement unit",

abstract = "This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.",

keywords = "Arm motion, HMM, Inertial measurement unit, Recognition, Segmentation, Wii remote",

author = "Takashi Aoki and Gentiane Venture and Dana Kuli{\'c}",

year = "2013",

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booktitle = "Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013",

address = "United States of America",

note = "IEEE International Conference on Systems, Man and Cybernetics 2013, SMC 2013 ; Conference date: 13-10-2013 Through 16-10-2013",

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Aoki, T, Venture, G & Kulić, D 2013, Segmentation of human body movement using inertial measurement unit. in Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013., 6721958, Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, IEEE, Institute of Electrical and Electronics Engineers, pp. 1181-1186, IEEE International Conference on Systems, Man and Cybernetics 2013, Manchester, United Kingdom, 13/10/13. https://doi.org/10.1109/SMC.2013.205

Segmentation of human body movement using inertial measurement unit. / Aoki, Takashi; Venture, Gentiane; Kulić, Dana.
Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1181-1186 6721958 (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013).

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

TY - GEN

T1 - Segmentation of human body movement using inertial measurement unit

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AU - Venture, Gentiane

AU - Kulić, Dana

PY - 2013/12/1

Y1 - 2013/12/1

N2 - This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.

AB - This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.

KW - Arm motion

KW - HMM

KW - Inertial measurement unit

KW - Recognition

KW - Segmentation

KW - Wii remote

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Aoki T, Venture G, Kulić D. Segmentation of human body movement using inertial measurement unit. In Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1181-1186. 6721958. (Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013). doi: 10.1109/SMC.2013.205

Segmentation of human body movement using inertial measurement unit

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Keywords

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