Vision-based fall detection through shape features

Chih Yang Lin; Shang Ming Wang; Jia Wei Hong; Li Wei Kang; Chung Lin Huang

doi:10.1109/BigMM.2016.22

Vision-based fall detection through shape features

Chih Yang Lin, Shang Ming Wang, Jia Wei Hong, Li Wei Kang, Chung Lin Huang

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

18 Citations (Scopus)

Abstract

A major cause of deaths among the elderly relates to accidental falls. Such falls are of particular medical concern to this population because they often result in severe injuries, since senior citizens usually live alone and cannot ask for help when accidents happen. In this paper, we propose a fall detection system with the help of a Gaussian mixture background model to build the background before motion history image (MHI) is applied to analyze the fall behavior. Finally, two extra features, acceleration and angular acceleration, are proposed to more accurately determine whether a fall event has happened.

Original language	English
Title of host publication	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	237-240
Number of pages	4
ISBN (Electronic)	9781509021789
DOIs	https://doi.org/10.1109/BigMM.2016.22
Publication status	Published - 2016 Aug 16
Externally published	Yes
Event	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan Duration: 2016 Apr 20 → 2016 Apr 22

Publication series

Name	Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

Conference

Conference	2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
Country/Territory	Taiwan
City	Taipei
Period	2016/04/20 → 2016/04/22

Keywords

Fall detection
Motion features
Motion history image

ASJC Scopus subject areas

Signal Processing
Information Systems
Media Technology

Access to Document

10.1109/BigMM.2016.22

Cite this

Lin, C. Y., Wang, S. M., Hong, J. W., Kang, L. W., & Huang, C. L. (2016). Vision-based fall detection through shape features. In Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016 (pp. 237-240). Article 7545029 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigMM.2016.22

Vision-based fall detection through shape features. / Lin, Chih Yang; Wang, Shang Ming; Hong, Jia Wei et al.
Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 237-240 7545029 (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016).

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

Lin, CY, Wang, SM, Hong, JW, Kang, LW & Huang, CL 2016, Vision-based fall detection through shape features. in Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016., 7545029, Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 237-240, 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016, Taipei, Taiwan, 2016/04/20. https://doi.org/10.1109/BigMM.2016.22

Lin CY, Wang SM, Hong JW, Kang LW, Huang CL. Vision-based fall detection through shape features. In Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 237-240. 7545029. (Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016). doi: 10.1109/BigMM.2016.22

@inproceedings{f1ae8d9404c44e1cbe6e5bc03fa0e6bb,

title = "Vision-based fall detection through shape features",

abstract = "A major cause of deaths among the elderly relates to accidental falls. Such falls are of particular medical concern to this population because they often result in severe injuries, since senior citizens usually live alone and cannot ask for help when accidents happen. In this paper, we propose a fall detection system with the help of a Gaussian mixture background model to build the background before motion history image (MHI) is applied to analyze the fall behavior. Finally, two extra features, acceleration and angular acceleration, are proposed to more accurately determine whether a fall event has happened.",

keywords = "Fall detection, Motion features, Motion history image",

author = "Lin, {Chih Yang} and Wang, {Shang Ming} and Hong, {Jia Wei} and Kang, {Li Wei} and Huang, {Chung Lin}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 ; Conference date: 20-04-2016 Through 22-04-2016",

year = "2016",

month = aug,

day = "16",

doi = "10.1109/BigMM.2016.22",

language = "English",

series = "Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016",

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

pages = "237--240",

booktitle = "Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016",

}

TY - GEN

T1 - Vision-based fall detection through shape features

AU - Lin, Chih Yang

AU - Wang, Shang Ming

AU - Hong, Jia Wei

AU - Kang, Li Wei

AU - Huang, Chung Lin

PY - 2016/8/16

Y1 - 2016/8/16

N2 - A major cause of deaths among the elderly relates to accidental falls. Such falls are of particular medical concern to this population because they often result in severe injuries, since senior citizens usually live alone and cannot ask for help when accidents happen. In this paper, we propose a fall detection system with the help of a Gaussian mixture background model to build the background before motion history image (MHI) is applied to analyze the fall behavior. Finally, two extra features, acceleration and angular acceleration, are proposed to more accurately determine whether a fall event has happened.

AB - A major cause of deaths among the elderly relates to accidental falls. Such falls are of particular medical concern to this population because they often result in severe injuries, since senior citizens usually live alone and cannot ask for help when accidents happen. In this paper, we propose a fall detection system with the help of a Gaussian mixture background model to build the background before motion history image (MHI) is applied to analyze the fall behavior. Finally, two extra features, acceleration and angular acceleration, are proposed to more accurately determine whether a fall event has happened.

KW - Fall detection

KW - Motion features

KW - Motion history image

UR - http://www.scopus.com/inward/record.url?scp=84987606983&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84987606983&partnerID=8YFLogxK

U2 - 10.1109/BigMM.2016.22

DO - 10.1109/BigMM.2016.22

M3 - Conference contribution

AN - SCOPUS:84987606983

T3 - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

SP - 237

EP - 240

BT - Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd IEEE International Conference on Multimedia Big Data, BigMM 2016

Y2 - 20 April 2016 through 22 April 2016

ER -

Vision-based fall detection through shape features

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this