SmokeMon. Alharbi, R., Shahi, S., Cruz, S., Li, L., Sen, S., Pedram, M., Romano, C., Hester, J., Katsaggelos, A., K., & Alshurafa, N. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(4):1-25, 2023.
doi  abstract   bibtex   
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.
@article{
 title = {SmokeMon},
 type = {article},
 year = {2023},
 keywords = {HAR,Smoking,Thermal,Wearable},
 pages = {1-25},
 volume = {6},
 chapter = {1},
 id = {802e5ec5-d687-3ee8-af1d-de4dbc9d1797},
 created = {2023-02-28T00:06:22.172Z},
 file_attached = {false},
 profile_id = {5f6ed621-5f14-3d88-8517-f63e186d1afc},
 group_id = {91adf249-127e-3b04-87b9-93a12088f0cb},
 last_modified = {2023-03-04T22:40:46.912Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Alharbi2023},
 source_type = {Journal Article},
 private_publication = {false},
 abstract = {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.},
 bibtype = {article},
 author = {Alharbi, Rawan and Shahi, Soroush and Cruz, Stefany and Li, Lingfeng and Sen, Sougata and Pedram, Mahdi and Romano, Christopher and Hester, Josiah and Katsaggelos, Aggelos K and Alshurafa, Nabil},
 doi = {10.1145/3569460},
 journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
 number = {4}
}
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