Abstract
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.
Highlights
According to the World Health Organization, smoking is the single most preventable cause of early death [1]
The generation of objective data about smoking patterns may enhance the efficacy of smoking cessation programs and contribute useful information about smoking behavior and relapse
The proposed approach validated the efficacy of combining the Inertial Measurement Unit (IMU) and electronic lighter for automatic monitoring of smoking
Summary
According to the World Health Organization, smoking is the single most preventable cause of early death [1]. Cigarette smoking causes ten percent of all annual deaths and increases the chances of many serious diseases [2]. Of the world’s annual gross domestic product [3]. These statistics underscore the important role of smoking cessation programs [4,5] to promote the economic, social, and, most importantly, health impact of quitting smoking. The first step of these cessation programs is to understand the patient’s smoking pattern over time. The generation of objective data about smoking patterns may enhance the efficacy of smoking cessation programs and contribute useful information about smoking behavior and relapse. The number of cigarettes consumed over a period time or biomarkers such as carbon monoxide and cotinine do not provide sufficient metrics for a detailed examination of Sensors 2019, 19, 570; doi:10.3390/s19030570 www.mdpi.com/journal/sensors
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