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Article

Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events

1
Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M15 6BH, UK
2
Department of Psychology, Manchester Metropolitan University, Manchester M15 6GX, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(4), 1099; https://doi.org/10.3390/s20041099
Received: 31 December 2019 / Revised: 10 February 2020 / Accepted: 13 February 2020 / Published: 17 February 2020
Nicotine consumption is considered a major health problem, where many of those who wish to quit smoking relapse. The problem is that overtime smoking as behaviour is changing into a habit, in which it is connected to internal (e.g., nicotine level, craving) and external (action, time, location) triggers. Smoking cessation apps have proved their efficiency to support smoking who wish to quit smoking. However, still, these applications suffer from several drawbacks, where they are highly relying on the user to initiate the intervention by submitting the factor the causes the urge to smoke. This research describes the creation of a combined Control Theory and deep learning model that can learn the smoker’s daily routine and predict smoking events. The model’s structure combines a Control Theory model of smoking with a 1D-CNN classifier to adapt to individual differences between smokers and predict smoking events based on motion and geolocation values collected using a mobile device. Data were collected from 5 participants in the UK, and analysed and tested on 3 different machine learning model (SVM, Decision tree, and 1D-CNN), 1D-CNN has proved it’s efficiency over the three methods with average overall accuracy 86.6%. The average MSE of forecasting the nicotine level was (0.04) in the weekdays, and (0.03) in the weekends. The model has proved its ability to predict the smoking event accurately when the participant is well engaged with the app. View Full-Text
Keywords: smoking cessation app; smoker’s behaviour; addictive behaviour; machine learning; deep learning; CNN; control theory smoking cessation app; smoker’s behaviour; addictive behaviour; machine learning; deep learning; CNN; control theory
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MDPI and ACS Style

Abo-Tabik, M.; Costen, N.; Darby, J.; Benn, Y. Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events. Sensors 2020, 20, 1099. https://doi.org/10.3390/s20041099

AMA Style

Abo-Tabik M, Costen N, Darby J, Benn Y. Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events. Sensors. 2020; 20(4):1099. https://doi.org/10.3390/s20041099

Chicago/Turabian Style

Abo-Tabik, Maryam; Costen, Nicholas; Darby, John; Benn, Yael. 2020. "Towards a Smart Smoking Cessation App: A 1D-CNN Model Predicting Smoking Events" Sensors 20, no. 4: 1099. https://doi.org/10.3390/s20041099

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