State Transition Modeling of the Smoking Behavior using LSTM Recurrent Neural Networks
This work addresses smoking behavior monitoring for healthcare applications, but it is incremental as it builds on previous detection methods by adding in-context recognition.
The study tackled the problem of recognizing smoking activity using smartwatch sensors by reformulating it as a state-transition model of mini-gestures, achieving detection rates nearing 100% with conventional neural networks and 97% accuracy with LSTM networks for in-context detection.
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%.