Research Article
Automated Detection of Puffing and Smoking with Wrist Accelerometers
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2014.254978, author={Qu Tang and Damon Vidrine and Eric Crowder and Stephen Intille}, title={Automated Detection of Puffing and Smoking with Wrist Accelerometers}, proceedings={8th International Conference on Pervasive Computing Technologies for Healthcare}, publisher={ICST}, proceedings_a={PERVASIVEHEALTH}, year={2014}, month={7}, keywords={behavior recognition health real-time smoking cigarette random forest supervised learning ubiquitous computing}, doi={10.4108/icst.pervasivehealth.2014.254978} }- Qu Tang
Damon Vidrine
Eric Crowder
Stephen Intille
Year: 2014
Automated Detection of Puffing and Smoking with Wrist Accelerometers
PERVASIVEHEALTH
ACM
DOI: 10.4108/icst.pervasivehealth.2014.254978
Abstract
Real-time, automatic detection of smoking behavior could lead to novel measurement tools for smoking research and “just-in-time” interventions that may help people quit, reducing preventable deaths. This paper discusses the use of machine learning with wrist accelerometer data for automatic puffing and smoking detection. A two-layer smoking detection model is proposed that incorporates both low-level time domain features and high-level smoking topography such as inter-puff intervals and puff frequency to detect puffing then smoking. On a pilot dataset of 6 individuals observed for 11.8 total hours in real-life settings performing complex tasks while smoking, the model obtains a cross validation F1-score of 0.70 for puffing detection and 0.79 for smoking detection over all participants, and a mean F1-score of 0.75 for puffing detection with user-specific training data. Unresolved challenges that must still be addressed in this activity detection domain are discussed.
