Lessons learned from designing an instrumented lighter for assessing smoking status

Scholl, Philipp M.; Laerhoven, Kristof Van

doi:10.1145/3123024.3124397

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Embedding sensors in commercially available cigarette lighters was introduced by Scholl et al [12] to detect lighting events prior to cigarette smoking. Three successive versions of an augmented lighter named UbiLighter [60] were introduced with an interfaced microcontroller configured to record lighter ignition events (with timestamps) in its internal memory. UbiLighter v1 (Figure 3a) was a modified electronic lighter in which closing electric contacts (by a slide down switch) triggered an interrupt to the microcontroller and heated the electric coil to ignite the lighter.…”

Section: Evaluation Of Sensing Methodologiesmentioning

confidence: 99%

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Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Imtiaz

Ramos-Garcia

Wattal

et al. 2019

Sensors

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Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers’ behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions.

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Section: Evaluation Of Sensing Methodologiesmentioning

confidence: 99%

“…The instrumented lighters: ( a ) UbiLighter v1; ( b ) UbiLighter v2; ( c ) UbiLighter v3; ( d ) the Personal Automatic Cigarette Tracker (PACT) lighter (image source: [60,61]).…”

Section: Figurementioning

confidence: 99%

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Imtiaz

Ramos-Garcia

Wattal

et al. 2019

Sensors

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“…A smart cigarette lighter detects when a cigarette is being lit before smoking. However, these lighters only capture and record instances of user’s smoking activity [18,19,20]; no details of the puff characteristics can be found from this information. Respiration sensors measure respiration patterns from chest contractions-expansions and attempt to detect the characteristic breathing patterns associated with smoke inhalation and exhalation [21,22,23,24,25].…”

Section: Introductionmentioning

confidence: 99%

Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter

Senyurek

Imtiaz

Belsare

et al. 2019

Sensors

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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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SmokeMon

Alharbi

Shahi

Cruz

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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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.

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Lessons learned from designing an instrumented lighter for assessing smoking status

Cited by 3 publications

References 4 publications

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter

SmokeMon

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