Lopez-Meyer P, Tiffany S, Yogendra P, Sazonov E, IEEE Trans Biomed Eng. (early access) 2013.
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 underreporting. This paper presents a first step in the development of a methodology for accurate and objective assessment of smoking using non-invasive 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 (SVM) classifiers. The performance of subjectdependent (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 hours or 21,411 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 non-invasive sensor system in free living conditions.