Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier.
This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.
Department of Electrical and computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA. plopezmeyer@ua.edu
Paulo
P
Lopez-Meyer
Stephen
S
Tiffany
Edward
E
Sazonov
D056044:Actigraphy / Q000295:instrumentation*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D012907:Smoking / Q000503:physiopathology*; D060388:Support Vector Machine; D013686:Telemetry / Q000295:instrumentation*; D055815:Young Adult