An audio-based hierarchical smoking behavior detection system based on a smart neckband platform

Cui, J, Wang, L, Gu, T, Tao, X and Lu, J 2016, 'An audio-based hierarchical smoking behavior detection system based on a smart neckband platform', in Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2016), Hiroshima, Japan, 28 November - 1 December 2016, pp. 190-199.


Document type: Conference Paper
Collection: Conference Papers

Title An audio-based hierarchical smoking behavior detection system based on a smart neckband platform
Author(s) Cui, J
Wang, L
Gu, T
Tao, X
Lu, J
Year 2016
Conference name MobiQuitous 2016: The 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2016)
Conference location Hiroshima, Japan
Conference dates 28 November - 1 December 2016
Proceedings title Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2016)
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 190
End page 199
Total pages 10
Abstract Smoking behavior detection has attracted much research interest for its significant impact on smokers' physical and mental health. Existing research has shown the potential of using wearable devices for fine-grained smoking puff and session detection by detecting a smoker's content of breathing, lighter usage, breathing, and gesture patterns. However, the existing systems are complex, and they are usually vulnerable to confounding activities and diversity of smoking behavior. To address these limitations, this paper proposes the design and implementation of a simple and compact smart neckband device for smoking detection. The device is equipped with both passive and active acoustic sensors to detect smoking sessions and puffs. We propose a hierarchical processing framework in which the lower-layer detects the sub-movements, i.e., lighter usage, hand-to-mouth gesture and deep breathing, from perceived audio data; and the higher-layer, based on the lower-layer , a´rs detection results, detects smoking puffs and sessions using temporal sequence analysis techniques. Real-world experiments suggest our system can accurately detect smoking puffs and sessions with F1 score of respectively 93.59% and 92.96% in complex environments with the presence of confounding activities and diverse ways of smoking.
Subjects Ubiquitous Computing
Mobile Technologies
Pattern Recognition and Data Mining
DOI - identifier 10.1145/2994374.2994384
Copyright notice © 2016 Association for Computing Machinery
ISBN 9781450347501
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