SmokeMon: Unobtrusive Extraction of Smoking Topography Using Wearable Energy-Efficient Thermal

Smoking is the leading cause of preventable death worldwide. Cigarette smoke includes thousands of chemicals that are harmful and cause tobacco-related diseases. To date, the causality between human exposure to specific compounds and the harmful effects is unknown. A first step in closing the gap in...

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Veröffentlicht in:Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies mobile, wearable and ubiquitous technologies, 2022-12, Vol.6 (4), p.1-25, Article 155
Hauptverfasser: Alharbi, Rawan, Shahi, Soroush, Cruz, Stefany, Li, Lingfeng, Sen, Sougata, Pedram, Mahdi, Romano, Christopher, Hester, Josiah, Katsaggelos, Aggelos K., Alshurafa, Nabil
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Sprache:eng
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Zusammenfassung:Smoking is the leading cause of preventable death worldwide. Cigarette smoke includes thousands of chemicals that are harmful and cause tobacco-related diseases. To date, the causality between human exposure to specific compounds and the harmful effects is unknown. A first step in closing the gap in knowledge has been measuring smoking topography, or how the smoker smokes the cigarette (puffs, puff volume, and duration). However, current gold-standard approaches to smoking topography involve expensive, bulky, and obtrusive sensor devices, creating unnatural smoking behavior and preventing their potential for real-time interventions in the wild. Although motion-based wearable sensors and their corresponding machine-learned models have shown promise in unobtrusively tracking smoking gestures, they are notorious for confounding smoking with other similar hand-to-mouth gestures such as eating and drinking. In this paper, we present SmokeMon, a chest-worn thermal-sensing wearable system that can capture spatial, temporal, and thermal information around the wearer and cigarette all day to unobtrusively and passively detect smoking events. We also developed a deep learning--based framework to extract puffs and smoking topography. We evaluate SmokeMon in both controlled and free-living experiments with a total of 19 participants, more than 110 hours of data, and 115 smoking sessions achieving an F1-score of 0.9 for puff detection in the laboratory and 0.8 in the wild. By providing SmokeMon as an open platform, we provide measurement of smoking topography in free-living settings to enable testing of smoking topography in the real world, with potential to facilitate timely smoking cessation interventions.
ISSN:2474-9567
2474-9567
DOI:10.1145/3569460