Human activity recognition from environmental background sounds for Wireless Sensor Networks

Yi Zhan; Shun Miura; Jun Nishimura; Tadahiro Kuroda

doi:10.1109/ICNSC.2007.372796

Human activity recognition from environmental background sounds for Wireless Sensor Networks

Yi Zhan, Shun Miura, Jun Nishimura, Tadahiro Kuroda

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

13 Citations (Scopus)

Abstract

Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and classification Dynamic Time Warping (DTW) algorithms are applied to recognizing the background sounds in the human daily activities. Applying these algorithms to fourteen typical daily activity sounds, average recognition accuracy of 92.5% can be achieved. In these algorithms, how two parameters (Le., Mel filters number and frame-to-frame overlap) affect system's calculation burden and accuracy is also investigated, By adjusting these two parameters to a suitable combination, the calculation burden can be reduced by 61.6% while maintaining the system's average accuracy rate at approximate 90%. This is promising for future integrating with other sensor(s) to fulfill daily activity recognition work by using power aware Wireless Sensor Networks (WSN) system.

Original language	English
Title of host publication	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Pages	307-312
Number of pages	6
DOIs	https://doi.org/10.1109/ICNSC.2007.372796
Publication status	Published - 2007 Oct 1
Event	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 - London, United Kingdom Duration: 2007 Apr 15 → 2007 Apr 17

Publication series

Name	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Other

Other	2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07
Country/Territory	United Kingdom
City	London
Period	07/4/15 → 07/4/17

Keywords

Calculation burden
DTW
MFCC
Sound recognition
WSN

ASJC Scopus subject areas

Computer Networks and Communications
Control and Systems Engineering

Access to Document

10.1109/ICNSC.2007.372796

Cite this

Zhan, Y., Miura, S., Nishimura, J., & Kuroda, T. (2007). Human activity recognition from environmental background sounds for Wireless Sensor Networks. In 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 (pp. 307-312). Article 4239009 (2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07). https://doi.org/10.1109/ICNSC.2007.372796

Human activity recognition from environmental background sounds for Wireless Sensor Networks. / Zhan, Yi; Miura, Shun; Nishimura, Jun et al.
2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07. 2007. p. 307-312 4239009 (2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07).

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

Zhan, Y, Miura, S, Nishimura, J & Kuroda, T 2007, Human activity recognition from environmental background sounds for Wireless Sensor Networks. in 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07., 4239009, 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07, pp. 307-312, 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07, London, United Kingdom, 07/4/15. https://doi.org/10.1109/ICNSC.2007.372796

@inproceedings{9467d7cee0c747f3af040611b913466a,

title = "Human activity recognition from environmental background sounds for Wireless Sensor Networks",

abstract = "Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and classification Dynamic Time Warping (DTW) algorithms are applied to recognizing the background sounds in the human daily activities. Applying these algorithms to fourteen typical daily activity sounds, average recognition accuracy of 92.5% can be achieved. In these algorithms, how two parameters (Le., Mel filters number and frame-to-frame overlap) affect system's calculation burden and accuracy is also investigated, By adjusting these two parameters to a suitable combination, the calculation burden can be reduced by 61.6% while maintaining the system's average accuracy rate at approximate 90%. This is promising for future integrating with other sensor(s) to fulfill daily activity recognition work by using power aware Wireless Sensor Networks (WSN) system.",

keywords = "Calculation burden, DTW, MFCC, Sound recognition, WSN",

author = "Yi Zhan and Shun Miura and Jun Nishimura and Tadahiro Kuroda",

year = "2007",

month = oct,

day = "1",

doi = "10.1109/ICNSC.2007.372796",

language = "English",

isbn = "1424410762",

series = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07",

pages = "307--312",

booktitle = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07",

note = "2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07 ; Conference date: 15-04-2007 Through 17-04-2007",

}

TY - GEN

T1 - Human activity recognition from environmental background sounds for Wireless Sensor Networks

AU - Zhan, Yi

AU - Miura, Shun

AU - Nishimura, Jun

AU - Kuroda, Tadahiro

PY - 2007/10/1

Y1 - 2007/10/1

N2 - Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and classification Dynamic Time Warping (DTW) algorithms are applied to recognizing the background sounds in the human daily activities. Applying these algorithms to fourteen typical daily activity sounds, average recognition accuracy of 92.5% can be achieved. In these algorithms, how two parameters (Le., Mel filters number and frame-to-frame overlap) affect system's calculation burden and accuracy is also investigated, By adjusting these two parameters to a suitable combination, the calculation burden can be reduced by 61.6% while maintaining the system's average accuracy rate at approximate 90%. This is promising for future integrating with other sensor(s) to fulfill daily activity recognition work by using power aware Wireless Sensor Networks (WSN) system.

AB - Sound feature extraction Mel Frequency Cepstral Coefficients (MFCC) and classification Dynamic Time Warping (DTW) algorithms are applied to recognizing the background sounds in the human daily activities. Applying these algorithms to fourteen typical daily activity sounds, average recognition accuracy of 92.5% can be achieved. In these algorithms, how two parameters (Le., Mel filters number and frame-to-frame overlap) affect system's calculation burden and accuracy is also investigated, By adjusting these two parameters to a suitable combination, the calculation burden can be reduced by 61.6% while maintaining the system's average accuracy rate at approximate 90%. This is promising for future integrating with other sensor(s) to fulfill daily activity recognition work by using power aware Wireless Sensor Networks (WSN) system.

KW - Calculation burden

KW - DTW

KW - MFCC

KW - Sound recognition

KW - WSN

UR - http://www.scopus.com/inward/record.url?scp=34748864703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34748864703&partnerID=8YFLogxK

U2 - 10.1109/ICNSC.2007.372796

DO - 10.1109/ICNSC.2007.372796

M3 - Conference contribution

AN - SCOPUS:34748864703

SN - 1424410762

SN - 9781424410767

T3 - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

SP - 307

EP - 312

BT - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

T2 - 2007 IEEE International Conference on Networking, Sensing and Control, ICNSC'07

Y2 - 15 April 2007 through 17 April 2007

ER -

Human activity recognition from environmental background sounds for Wireless Sensor Networks

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this