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, sever
al
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.