Monitoring smoking behaviour using a wearable acoustic sensor.

Smoking is a cause of multiple health problems resulting in diseases which can also be fatal. It is well known that smoking has long-term impact on the health of an individual as well. While a number of studies have looked at the impact of smoking on health and its economic impacts, most of these rely on input from smokers in the form of questionnaires and surveys. Long-term monitoring of smoking habits and behaviour is thus not possible because of the lack of means to do so. This paper proposes the use of a wearable device to monitor breathing signals of subjects. It is shown that the acoustic properties of a smoking breath are different from a non-smoking breath. To encapsulate these differences, several features from a breath segment are extracted and used with a simple classifier to automatically identify smoking breaths. The proposed algorithm detected smoking and non-smoking breaths with average accuracy of 66% and 99% respectively.