Diseases resulting from prolonged smoking are the most common preventable causes of death in the world today. A great deal of research is being conducted to find comprehensive, effective ways to aid in cessation of smoking. There are a myriad of websites that provide services to smokers such as access to resources, online support and help lines. However, most of these sites do not provide a mobile app for an increasingly mobile population. In recent years smoking cessation mobile applications have also been developed. Most of these apps simply allow the user to enter in the amount of cigarettes that they usually would smoke in a day and then "self-report" any cigarettes that they did smoke and then calculate the money that they would have saved if they hadn't smoked. This self-reporting process is usually unreliable because users cannot always be trusted to keep up with inputing data. Currently our lab is developing an android based app for smartwatches that will automatically detect smoking gestures thus bypassing the issue of self-reporting. Furthermore, our partners in Public Health are looking at ways to utilize our app to provide more meaningful intervention for the user. Our detection mechanism utilizes custom built pattern recognition models in conjunction with accelerometer data directly collected from the smartwatch to detect smoking gestures.
Casey A. Cole, Bethany Janos, Dien Anshari, James F. Thrasher, Scott Strayer, Homayoun Valafar, Recognition of Smoking Gesture Using Smart Watch Technology, Proceedings of the International Conference on Health Informatics and Medical Systems (HIMS), July 2016, Las Vegas, NV