Paper Info
Title
Identifying Smoking from Smartphone Sensor Data and Multivariate Hidden Markov Models.
Abstract
Smoking is one of the foremost public health threats listed by the World Health Organization, and surveillance is a key to informing effective policies. High smartphone penetration and mature smartphone sensor data collecting techniques make smartphone sensor data based smoking monitoring viable, yet an effective classification algorithm remains elusive. In this paper, we sought to classify smoking using multivariate Hidden Markov models (HMMs) informed by binned time-series of transformed sensor data collected with smartphone-based Wi-Fi, GPS, and accelerometer sensors. Our model is trained on smartphone sensor time series data labeled with self-reported smoking periods. Two-fold cross-validation shows Az (area under receiver operating characteristic curve) for HMMs using five features = (0.52, 0.84). Comparison of univariate HMMs and multivariate HMMs, suggests a high accuracy of multivariate HMMs for smoking periods classification.
Year
DOI
Venue
2017
10.1007/978-3-319-60240-0_27
Lecture Notes in Computer Science
Keywords
Field
DocType
Hidden Markov model,Smartphone sensor data,Tobacco,Smoking monitoring
Time series,Data mining,Internet privacy,Receiver operating characteristic,Computer science,Multivariate statistics,Accelerometer,Global Positioning System,Univariate,Hidden Markov model
Conference
Volume
ISSN
Citations 
10354
0302-9743
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Yang Qin101.01
Weicheng Qian2233.61
Narjes Shojaati321.10
Nathaniel D. Osgood4239.92