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Non-invasive sensor based automated smoking activity detection

conference contribution
posted on 2017-07-01, 00:00 authored by B Bhandari, JianChao Lu, Xi Zheng, Sutharshan RajasegararSutharshan Rajasegarar, Chandan KarmakarChandan Karmakar
Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation. However, these methods are inefficient since they lack in providing real time personalized intervention messages to the smoking addicted users. To address this challenge, the first step is to build an automated smoking behavior detection system. In this study, we propose an accelerometer sensor based non-invasive and automated framework for smoking behavior detection. We built a prototype device to collect data from several participants performing smoking and other five confounding activities. We used three different classifiers to compare activity detection performance using the extracted features from accelerometer data. Our evaluation demonstrates that the proposed approach is able to classify smoking activity among the confounding activities with high accuracy. The proposed system shows the potential for developing a real time automated smoking activity detection and intervention framework.

History

Pagination

845-848

Location

Seogwipo, South Korea

Start date

2017-07-11

End date

2017-07-15

ISSN

1557-170X

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

EMBC 2017 : Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Event

Engineering in Medicine and Biology Society. Annual International Conference (39th : 2017 : Seogwipo, South Korea)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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