@inproceedings{815f7709c8ce4368ab06a37b88941cd8,
title = "Automated detection of puffing and smoking with wrist accelerometers",
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 Fl-score of 0.70 for puffing detection and 0.79 for smoking detection over all participants, and a mean Fl-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.",
keywords = "Behavior recognition, Cigarette, Health, Random forest, Real-time, Smoking, Supervised learning, Ubiquitous computing",
author = "Qu Tang and Vidrine, {Damon J.} and Eric Crowder and Intille, {Stephen S.}",
note = "Publisher Copyright: Copyright {\textcopyright} 2014 ICST.; 8th International Conference on Pervasive Computing Technologies for Healthcare, PERVASIVEHEALTH 2014 ; Conference date: 20-05-2014 Through 23-05-2014",
year = "2014",
month = jul,
day = "23",
doi = "10.4108/icst.pervasivehealth.2014.254978",
language = "English (US)",
series = "Proceedings - PERVASIVEHEALTH 2014: 8th International Conference on Pervasive Computing Technologies for Healthcare",
publisher = "ICST",
pages = "80--87",
editor = "Susanne Boll and Friedrich Kohler",
booktitle = "Proceedings - PERVASIVEHEALTH 2014",
}