Long Short-Term Memory Based Model for Modeling Nicotine Consumption Using an Electronic Cigarette and Internet of Things Devices
In this paper, we want to determine whether the accurate prediction of nicotine concentration can be obtained by using a network of smart objects and an e-cigarette. The approach consists of, first, the recognition of factors influencing smoking cessation such as physical activity recognition and participant’s behaviors (using both smartphone and smartwatch), then the prediction of the configuration of the e-cigarette (in terms of nicotine concentration, power, and resistance of e-cigarette). The study uses a network of commonly connected objects; a smartwatch, a smartphone, and an e-cigarette transported by the participants during an uncontrolled experiment. The data obtained from sensors carried in the three devices were trained by a Long short-term memory algorithm (LSTM). Results show that our LSTM-based model allows predicting the configuration of the e-cigarette in terms of nicotine concentration, power, and resistance with a root mean square error percentage of 12.9%, 9.15%, and 11.84%, respectively. This study can help to better control consumption of nicotine and offer an intelligent configuration of the e-cigarette to users.
 VIA. Bayat, M. Pomplun, and D. A. Tran, "A study on human activity recognition using accelerometer data from smartphones," Procedia Computer Science, vol. 34, pp. 450-457, 2014.
 R. Bittoun and others, "A combination nicotine replacement therapy (NRT) algorithm for hard-to-treat smokers," Journal of Smoking Cessation, vol. 1, p. 3, 2006 2006.
 J. Brown, E. Beard, D. Kotz, S. Michie, and R. West, "Real-world effectiveness of e-cigarettes when used to aid smoking cessation: a cross-sectional population study," Addiction, vol. 109, pp. 1531–1540, 2014 2014.
 B. B. Hoeppner, S. S. Hoeppner, L. Seaboyer, M. R. Schick, G. W. Wu, B. G. Bergman, and J. F. Kelly, "How smart are smartphone apps for smoking cessation," A content analysis Nicotine Tob Res, 2015 2015.
 M. Mironidou-Tzouveleki, E. Tzitzi, and P. Tzitzis, "Electronic Cigarette (E-Cig) as a Way of Smoking Cessation: Benefits and Potential Harms," Journal of Drug Addiction, Education, and Eradication, vol. 11, p. 235, 2015 2015.
 B. Dautzenberg and D. Bricard, "Real-time characterization of e-cigarettes use: the 1 million puffs study," J Addict Res Ther, vol. 6, 2015.
 M. SEIGEL and others, "Electronic Cigarettes as a smoking Cessation Tool," American Journal of Preventive Medicine, vol. 40, 2011 2011.
 P. E. Grebenstein, D. Burroughs, S. A. Roiko, P. R. Pentel, and M. G. LeSage, "Predictors of the nicotine reinforcement threshold, compensation, and elasticity of demand in a rodent model of nicotine reduction policy," Drug and alcohol dependence, vol. 151, pp. 181–193, 2015 2015.
 P. Batel, F. Pessione, C. Maitre, and B. Rueff, "Relationship between alcohol and tobacco dependencies among alcoholics who smoke," Addiction, vol. 90, pp. 977-980, 1995.
 R. Bryson, P. M. Biner, E. McNair, M. Bergondy, and O. R. Abrams, "Effects of nicotine on two types of motor activity in rats," Psychopharmacology, vol. 73, pp. 168-170, 1981.
 P. C. Hatchell and A. C. Collins, "The influence of genotype and sex on behavioral sensitivity to nicotine in mice," Psychopharmacology, vol. 71, pp. 45-49, 1980.
 A. H. Taylor, M. H. Ussher, and G. Faulkner, "The acute effects of exercise on cigarette cravings, withdrawal symptoms, affect and smoking behaviour: a systematic review," Addiction, vol. 102, pp. 534-543, 2007.
 M. Todd, "Daily processes in stress and smoking: effects of negative events, nicotine dependence, and gender," Psychology of Addictive Behaviors, vol. 18, p. 31, 2004.
 D. Momperousse, C. Delnevo, and M. Lewis, "Exploring the seasonality of cigarette-smoking behaviour," Tobacco control, vol. 16, pp. 69-70, 2007.
 H. Wang and D.-Y. Yeung, "Towards Bayesian Deep Learning: A Survey," arXiv preprint arXiv:1604.01662, 2016.
 Y. Gal, "ModernDeepLearningthroughBayesianEyes."
 J. D. McCaffrey. (2015). Particle Swarm Optimization using Python. Available: https://jamesmccaffrey.wordpress.com/2015/06/09/particle-swarm-optimization-using-python/.
 "Bayesian networks: Inference and learning," ed, 2011.
 K. Patel, J. Vala, and J. Pandya, "Comparison of various classification algorithms on iris datasets using WEKA," Int. J. Adv. Eng. Res. Dev.(IJAERD), vol. 1, 2014.
 J. R. Kwapisz, G. M. Weiss, and S. A. Moore, "Activity recognition using cell phone accelerometers," ACM SigKDD Explorations Newsletter, vol. 12, pp. 74-82, 2011.
 T. Saponas, J. Lester, J. Froehlich, J. Fogarty, and J. Landay, "ilearn on the iphone: Real-time human activity classification on commodity mobile phones," University of Washington CSE Tech Report UW-CSE-08-04-02, vol. 2008, 2008.
 A. M. Khan, Y.-K. Lee, S. Lee, and T.-S. Kim, "Human activity recognition via an accelerometer-enabled-smartphone using kernel discriminant analysis," in Future Information Technology (FutureTech), 2010 5th International Conference on, 2010, pp. 1-6.
 T. M. T. Do and D. Gatica-Perez, "Groupus: Smartphone proximity data and human interaction type mining," in Wearable Computers (ISWC), 2011 15th Annual International Symposium on, 2011, pp. 21-28.
 H. Amroun, N. Ouarti, and M. A. M’Hamed Hamy Temkit, "Les transitions de positions du smartphone, quelles conséquences sur la reconnaissance de l’activité humaine?," 2017.
 J. Salamon and J. P. Bello, "Unsupervised feature learning for urban sound classification," in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, 2015, pp. 171-175.
 P. Hough, "‘Make Goals Not War’: The Contribution of International Football to World Peace," The International journal of the History of Sport, vol. 25, pp. 1287-1305, 2008.
 G. Montavon, "Deep learning for spoken language identification," in NIPS Workshop on deep learning for speech recognition and related applications, 2009, pp. 1-4.
 N. A. Boukary, "A comparison of time series forecasting learning algorithms on the task of predicting event timing," Ph. D. dissertation, Royal Military College of Canada, 2016.