ArticleBiomedical signal processing and control2019
Smoking detection based on regularity analysis of hand to mouth gestures.
Article in Biomedical signal processing and control, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review.Sensors (Basel, Switzerland) · 2019Pooled it
- Sensors for Smoking Detection in Epidemiological Research: Scoping Review.JMIR mHealth and uHealth · 2024Article
- HabitSense: A Privacy-Aware, AI-Enhanced Multimodal Wearable Platform for mHealth Applications.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Validation of the Glover-Nilsson Smoking Behavioral Questionnaire (GN-SBQ) to Evaluate Nicotine Dependence in Spanish Clinical Settings.International journal of environmental research and public health · 2023Article
- SmokeMon: Unobtrusive Extraction of Smoking Topography Using Wearable Energy-Efficient Thermal.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2022Article
- CNN-Based Smoker Classification and Detection in Smart City Application.Sensors (Basel, Switzerland) · 2022Article
- Article
- Wearable Egocentric Camera as a Monitoring Tool of Free-Living Cigarette Smoking: A Feasibility Study.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2020Article
- Computation of Cigarette Smoke Exposure Metrics From Breathing.IEEE transactions on bio-medical engineering · 2020Article
- A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors.Biomedical engineering letters · 2020Article
- A Report on Smoking Detection and Quitting Technologies.International journal of environmental research and public health · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
A number of studies have been introduced for the detection of smoking via a variety of features extracted from the wrist IMU data. However, none of the previous studies investigated gesture regularity as a way to detect smoking events. This study describes a novel method to detect smoking events by monitoring the regularity of hand gestures. Here, the regularity of hand gestures was estimated from a one axis accelerometer worn on the wrist of the dominant hand. To quantify the regularity score, this paper applied a novel approach of unbiased autocorrelation to process the temporal sequence of hand gestures. The comparison of regularity score of smoking events with other activities substantiated that hand-to-mouth gestures are highly regular during smoking events and have the potential to detect smoking from among a plethora of daily activities. This hypothesis was validated on a dataset of 140 cigarette smoking events generated by 35 regular smokers in a controlled setting. The regularity of gestures detected smoking events with an F1-score of 0.81. However, the accuracy dropped to 0.49 in the free-living study of same 35 smokers smoking 295 cigarettes. Nevertheless, regularity of gestures may be useful as a supportive tool for other detection methods. To validate that proposition, this paper further incorporated the regularity of gestures in an instrumented lighter based smoking detection algorithm and achieved an improvement in F1-score from 0.89 (lighter only) to 0.91 (lighter and regularity of hand gestures).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.