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Monitoring of Cigarette Smoking Using Wearable Sensors and Support Vector Machines

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4 Author(s)
Lopez-Meyer, P. ; Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA ; Tiffany, S. ; Patil, Y. ; Sazonov, E.

Cigarette smoking is a serious risk factor for cancer, cardiovascular, and pulmonary diseases. Current methods of monitoring of cigarette smoking habits rely on various forms of self-report that are prone to errors and under reporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using noninvasive wearable sensors (Personal Automatic Cigarette Tracker - PACT) by demonstrating feasibility of automatic recognition of smoke inhalations from signals arising from continuous monitoring of breathing and hand-to-mouth gestures by support vector machine classifiers. The performance of subject-dependent (individually calibrated) models was compared to performance of subject-independent (group) classification models. The models were trained and validated on a dataset collected from 20 subjects performing 12 different activities representative of everyday living (total duration 19.5 h or 21411 breath cycles). Precision and recall were used as the accuracy metrics. Group models obtained 87% and 80% of average precision and recall, respectively. Individual models resulted in 90% of average precision and recall, indicating a significant presence of individual traits in signal patterns. These results suggest the feasibility of monitoring cigarette smoking by means of a wearable and noninvasive sensor system in free living conditions.

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Biomedical Engineering, IEEE Transactions on  (Volume:60 ,  Issue: 7 )