  The use of sensors has pervaded everyday life in several applications
including human activity monitoring, healthcare, and social networks. In this
study, we focus on the use of smartwatch sensors to recognize smoking activity.
More specifically, we have reformulated the previous work in detection of
smoking to include in-context recognition of smoking. Our presented
reformulation of the smoking gesture as a state-transition model that consists
of the mini-gestures hand-to-lip, hand-on-lip, and hand-off-lip, has
demonstrated improvement in detection rates nearing 100% using conventional
neural networks. In addition, we have begun the utilization of Long-Short-Term
Memory (LSTM) neural networks to allow for in-context detection of gestures
with accuracy nearing 97%.
