PACT CAM: Wearable Sensor System to Capture the Details of Cigarette Smoking in Free-Living

Author(s):  
Masudul H Imtiaz ◽  
Delwar Hossain ◽  
Volkan Y Senyurek ◽  
Prajakta Belsare ◽  
Edward Sazonov
2013 ◽  
Vol 74 (6) ◽  
pp. 956-964 ◽  
Author(s):  
Edward Sazonov ◽  
Paulo Lopez-Meyer ◽  
Stephen Tiffany

2019 ◽  
Vol 22 (10) ◽  
pp. 1883-1890 ◽  
Author(s):  
Masudul H Imtiaz ◽  
Delwar Hossain ◽  
Volkan Y Senyurek ◽  
Prajakta Belsare ◽  
Stephen Tiffany ◽  
...  

Abstract Introduction Wearable sensors may be used for the assessment of behavioral manifestations of cigarette smoking under natural conditions. This paper introduces a new camera-based sensor system to monitor smoking behavior. The goals of this study were (1) identification of the best position of sensor placement on the body and (2) feasibility evaluation of the sensor as a free-living smoking-monitoring tool. Methods A sensor system was developed with a 5MP camera that captured images every second for continuously up to 26 hours. Five on-body locations were tested for the selection of sensor placement. A feasibility study was then performed on 10 smokers to monitor full-day smoking under free-living conditions. Captured images were manually annotated to obtain behavioral metrics of smoking including smoking frequency, smoking environment, and puffs per cigarette. The smoking environment and puff counts captured by the camera were compared with self-reported smoking. Results A camera located on the eyeglass temple produced the maximum number of images of smoking and the minimal number of blurry or overexposed images (53.9%, 4.19%, and 0.93% of total captured, respectively). During free-living conditions, 286,245 images were captured with a mean (±standard deviation) duration of sensor wear of 647(±74) minutes/participant. Image annotation identified consumption of 5(±2.3) cigarettes/participant, 3.1(±1.1) cigarettes/participant indoors, 1.9(±0.9) cigarettes/participant outdoors, and 9.02(±2.5) puffs/cigarette. Statistical tests found significant differences between manual annotations and self-reported smoking environment or puff counts. Conclusions A wearable camera-based sensor may facilitate objective monitoring of cigarette smoking, categorization of smoking environments, and identification of behavioral metrics of smoking in free-living conditions. Implications The proposed camera-based sensor system can be employed to examine cigarette smoking under free-living conditions. Smokers may accept this unobtrusive sensor for extended wear, as the sensor would not restrict the natural pattern of smoking or daily activities, nor would it require any active participation from a person except wearing it. Critical metrics of smoking behavior, such as the smoking environment and puff counts obtained from this sensor, may generate important information for smoking interventions.


2018 ◽  
Vol 56 (3) ◽  
pp. 228-240 ◽  
Author(s):  
Hanne AUSTAD ◽  
Øystein WIGGEN ◽  
Hilde FÆREVIK ◽  
Trine M. SEEBERG

2018 ◽  
Vol 2 (2) ◽  
pp. 27 ◽  
Author(s):  
Juan Haladjian ◽  
Johannes Haug ◽  
Stefan Nüske ◽  
Bernd Bruegge

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 570 ◽  
Author(s):  
Volkan Senyurek ◽  
Masudul Imtiaz ◽  
Prajakta Belsare ◽  
Stephen Tiffany ◽  
Edward Sazonov

In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5–2 h) and unconstrained free-living conditions (~24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.


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