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Detection of Smoking Events from Confounding Activities of Daily Living

Published: 29 January 2019 Publication History

Abstract

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 such as counselling program, motivational interview and pharmacotherapy. However, these methods lack providing real time personalized intervention messages to the smoking addicted users. The challenge is to develop an automated smoking behavior detection. We address this challenge by proposing a non-invasive sensor based automated framework for smoking behavior detection. We used a wristband based accelerometer and gyroscope sensors to detect smoking activities, differentiating with the closely confounding activities. We extract several features using learning algorithms and the empirical results with our participants show good accuracy in detecting the smoking activity in terms of precision, recall, and Flscore.

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cover image ACM Other conferences
ACSW '19: Proceedings of the Australasian Computer Science Week Multiconference
January 2019
486 pages
ISBN:9781450366038
DOI:10.1145/3290688
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • CORE - Computing Research and Education
  • Macquarie University-Sydney

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 January 2019

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Author Tags

  1. Activity recognition
  2. Mobile and wearable computing systems and services
  3. Pervasive technologies for healthcare

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  • Research-article
  • Research
  • Refereed limited

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ACSW 2019
ACSW 2019: Australasian Computer Science Week 2019
January 29 - 31, 2019
NSW, Sydney, Australia

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ACSW '19 Paper Acceptance Rate 61 of 141 submissions, 43%;
Overall Acceptance Rate 61 of 141 submissions, 43%

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  • (2023)An Empirical Study on Model Pruning and QuantizationBroadband Communications, Networks, and Systems10.1007/978-3-031-40467-2_7(111-125)Online publication date: 30-Jul-2023
  • (2022)A Review of IoT-Enabled Mobile Healthcare: Technologies, Challenges, and Future TrendsIEEE Internet of Things Journal10.1109/JIOT.2022.31444009:12(9478-9502)Online publication date: 15-Jun-2022
  • (2022)Machine Learning Aided Minimal Sensor based Hand Gesture Character Recognition2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA54385.2022.10032406(1-9)Online publication date: 13-Oct-2022
  • (2022)RFMonitorComputer Communications10.1016/j.comcom.2021.12.018185:C(55-65)Online publication date: 1-Mar-2022
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