A mobile app based smoking cessation assistance using automated detection of smoking activity

ABSTRACT

The rising prevalence of non-communicable diseases like cardiovascular diseases, stroke, chronic obstructive pulmonary disease, cancer is a serious threat to the society. Tobacco smoking is one of the most prevailing risk factors. Due to its addictiveness, it is often very difficult to quit. Abstinent smokers often start with a sudden craving for smoking which results in lapse and then permanent relapse. In this paper, we propose a sensor-based approach for automated recognition of smoking activity. That may be used for providing interventions in near real-time via mobile app to promote smoking cessation. A 6-axis inertial sensor along with a heart rate sensor is to be used to develop a wearable band which could be worn on the wrist. A pilot study is conducted with four participants. Their hand movement pattern is recorded for around 5 minutes for smoking and non-smoking intervals each, using a sensor based unit worn on the wrist. This period includes smoking and non-smoking intervals. Preliminary analysis of the data shows that there exists a periodicity in the data during the smoking episode. During non-smoking interval the sensor signals are random and does not exhibit such periodicity. Further data collection with more number of participants in different environments, data preprocessing, analysis, training, model generation, and testing is under progress. Preliminary results of this pilot study have been discussed.

References

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by classification

• Human-centered computing

  • Ubiquitous and mobile computing

    • Ubiquitous and mobile computing systems and tools

    • Ubiquitous and mobile computing theory, concepts and paradigms

      • Ambient intelligence

    • Ubiquitous and mobile devices

      • Mobile devices