2020/03/05 by Casey A. Cole, Bethany Janos, Cole, Casey A. +10
Computer Science · Engineering · Mathematics · Medicine · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Non-Invasive Vital Sign Monitoring #Smoking Behavior and Cessation #Video Surveillance and Tracking Methods #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2003.02735
7 pages, Published originally at HIMS in 2016
arxiv created 2020/03/05 · openalex publication_date 2020/03/05 · arxiv updated 2020/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Diseases resulting from prolonged smoking are the most common preventable causes of death in the world today. In this report we investigate the success of utilizing accelerometer sensors in smart watches to identify smoking gestures. Early identification of smoking gestures can help to initiate the appropriate intervention method and prevent relapses in smoking. Our experiments indicate 85%-95% success rates in identification of smoking gesture among other similar gestures using Artificial Neural Networks (ANNs). Our investigations concluded that information obtained from the x-dimension of accelerometers is the best means of identifying the smoking gesture, while y and z dimensions are helpful in eliminating other gestures such as: eating, drinking, and scratch of nose. We utilized sensor data from the Apple Watch during the training of the ANN. Using sensor data from another participant collected on Pebble Steel, we obtained a smoking identification accuracy of greater than 90% when using an ANN trained on data previously collected from the Apple Watch. Finally, we have demonstrated the possibility of using smart watches to perform continuous monitoring of daily activities.