TY - GEN
T1 - Understanding smoking behavior using wearable sensors
T2 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
AU - Patil, Yogendra
AU - Tiffany, Stephen
AU - Sazonov, Edward
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/2
Y1 - 2014/11/2
N2 - The Personal Automatic Cigarette Tracker (PACT) system, which consists of abdominal (AB) and thoracic (TC) breathing sensors, and a RF hand-to-mouth proximity sensor (PS), has proven to be useful in the detection and characterization of cigarette smoke inhalations. In this research, we further analyze the impact of subjects' anthropometric characteristics on the quality of sensor signals and evaluate the contribution of each sensor modality to the accuracy of the classifier for smoke inhalations detection. Results indicated that subjects with medium BMI, high BMI, and in a standing position were, respectively, 1.91, 4.74 and 4.32 times more likely to affect the quality of the breathing signal. Features extracted from TC+AB+PS, TC, AB, and PS sensors for individual detection models, resulted in F-scores of 94%, 85.39%, 88.54% and 90.48% respectively. For group models, the F-scores were 67.12%, 41.46%, 46.56% and 59.14%. This indicates higher contribution of abdominal breathing and hand gestures to detection of smoke inhalations.
AB - The Personal Automatic Cigarette Tracker (PACT) system, which consists of abdominal (AB) and thoracic (TC) breathing sensors, and a RF hand-to-mouth proximity sensor (PS), has proven to be useful in the detection and characterization of cigarette smoke inhalations. In this research, we further analyze the impact of subjects' anthropometric characteristics on the quality of sensor signals and evaluate the contribution of each sensor modality to the accuracy of the classifier for smoke inhalations detection. Results indicated that subjects with medium BMI, high BMI, and in a standing position were, respectively, 1.91, 4.74 and 4.32 times more likely to affect the quality of the breathing signal. Features extracted from TC+AB+PS, TC, AB, and PS sensors for individual detection models, resulted in F-scores of 94%, 85.39%, 88.54% and 90.48% respectively. For group models, the F-scores were 67.12%, 41.46%, 46.56% and 59.14%. This indicates higher contribution of abdominal breathing and hand gestures to detection of smoke inhalations.
UR - https://www.scopus.com/pages/publications/84929501243
U2 - 10.1109/EMBC.2014.6945214
DO - 10.1109/EMBC.2014.6945214
M3 - Conference contribution
C2 - 25571582
AN - SCOPUS:84929501243
T3 - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
SP - 6899
EP - 6902
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 August 2014 through 30 August 2014
ER -