Conference Paper2012Cited by 31

mPuff: automated detection of cigarette smoking puffs from respiration measurements

Amin Ahsan Ali, Syed Monowar Hossain, Karen Hovsepian, Md Mahbubur Rahman, Kurt Plarre, Santosh Kumar

Proceedings of the 11th international conference on Information Processing in Sensor Networks

IEEE, pp. 269–280

  • Good Health and Well-being — Relevance: 89%
DOI: 10.1109/ipsn.2012.6920942URL

Abstract

Smoking has been conclusively proved to be the leading cause of mortality that accounts for one in five deaths in the United States. Extensive research is conducted on developing effective smoking cessation programs. Most smoking cessation programs achieve low success rate because they are unable to intervene at the right moment. Identification of high-risk situations that may lead an abstinent smoker to relapse involve discovering the associations among various contexts that precede a smoking session or a smoking lapse. In the absence of an automated method, detection of smoking events still relies on subject self-report that is prone to failure to report and involves subject burden. Automated detection of smoking events in the natural environment can revolutionize smoking research and lead to effective intervention. In this paper, we present mPuff a novel system to automatically detect smoking puffs from respiration measurements, using which a model can be developed to automatically detect entire smoking episodes in the field. We introduce several new features from respiration that can help classify individual respiration cycles into smoking puffs or non-puffs. We then propose supervised and semi-supervised support vector models to detect smoking puffs. We train our models on data collected from 10 daily smokers and find that smoking puffs can be detected with an accuracy of 91% within a smoking session. We then consider respiration measurements during confounding events such as stress, speaking, and walking, and show that our model can still identify smoking puffs with an accuracy of 86.7%. The smoking detector presented here opens the opportunity to develop effective interventions that can be delivered on a mobile phone when and where smoking urges may occur, thereby improving the abysmal low rate of success in smoking cessation.

Keywords

  • respiration measurement
  • semi-supervised learning
  • Support vector machine
  • smoking relapse
  • Smoking cessation
  • mobile health intervention
  • smoking urges

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

References

  1. 1.(2007). Pattern Recognition and Machine Learning. Kybernetes, 36(2), 275[10.1108/03684920710743466]
  2. 2.Ali H. Mokdad. (2004). Actual Causes of Death in the United States, 2000. JAMA, 291(10), 1238[10.1001/jama.291.10.1238]
  3. 3.Xiaojin Zhu, Andrew B. Goldberg. (2009). Introduction to Semi-Supervised Learning. Synthesis lectures on artificial intelligence and machine learning[10.1007/978-3-031-01548-9]
  4. 4.John R. Hughes. (1986). Signs and Symptoms of Tobacco Withdrawal. Archives of General Psychiatry, 43(3), 289[10.1001/archpsyc.1986.01800030107013]
  5. 5.(2009). Smoking-Attributable Mortality, Years of Potential Life Lost, and Productivity Losses—United States, 2000-2004. JAMA, 301(6), 593[10.1001/jama.301.6.593]
  6. 6.Saul Shiffman, Jean A. Paty, Maryann Gnys, Jon A. Kassel, Mary Hickcox. (1996). First lapses to smoking: Within-subjects analysis of real-time reports.. Journal of Consulting and Clinical Psychology, 64(2), 366–379[10.1037/0022-006x.64.2.366]
  7. 7.Saul Shiffman, Andrew J. Waters. (2004). Negative Affect and Smoking Lapses: A Prospective Analysis.. Journal of Consulting and Clinical Psychology, 72(2), 192–201[10.1037/0022-006x.72.2.192]
  8. 8.Joel D. Killen, Stephen P. Fortmann. (1997). Craving is associated with smoking relapse: Findings from three prospective studies.. Experimental and Clinical Psychopharmacology, 5(2), 137–142[10.1037/1064-1297.5.2.137]
  9. 9.Kurt Plarre, Andrew Raij, Syed Monowar Hossain, Amin Ahsan Ali, Motohiro Nakajima, Mustafa Al’Absi, Emre Ertin, Thomas W. Kamarck, Santosh Kumar, Marcia S. Scott, Daniel P. Siewiorek, Asim Smailagic, Lorentz E. Wittmers. (2011). Continuous inference of psychological stress from sensory measurements collected in the natural environment. Information Processing in Sensor Networks, 97–108
  10. 10.Emre Ertin, Nathan Stohs, Santosh Kumar, Andrew Raij, Mustafa al'Absi, Siddharth Shah. (2011). AutoSense. , 274–287[10.1145/2070942.2070970]
  11. 11.K. Doherty, T. Kinnunen, F. S. Militello, A. J. Garvey. (1995). Urges to smoke during the first month of abstinence: relationship to relapse and predictors. Psychopharmacology, 119(2), 171–178[10.1007/bf02246158]
  12. 12.David Hammond, Geoffrey T. Fong, K. Michael Cummings, Andrew Hyland. (2005). Smoking Topography, Brand Switching, and Nicotine Delivery: Results from an <i>In vivo</i> Study. Cancer Epidemiology Biomarkers & Prevention, 14(6), 1370–1375[10.1158/1055-9965.epi-04-0498]
  13. 13.Saul Shiffman, Deborah M. Scharf, William G. Shadel, Chad J. Gwaltney, Qianyu Dang, Stephanie M. Paton, Duncan B. Clark. (2006). Analyzing milestones in smoking cessation: Illustration in a nicotine patch trial in adult smokers.. Journal of Consulting and Clinical Psychology, 74(2), 276–285[10.1037/0022-006x.74.2.276]
  14. 14.David Kalman. (2002). The subjective effects of nicotine: methodological issues, a review of experimental studies, and recommendations for future research. Nicotine & Tobacco Research, 4(1), 25–70[10.1080/14622200110098437]
  15. 15.Gary E. Swan, Marcia M. Ward, Lisa M. Jack. (1996). Abstinence effects as predictors of 28-day relapse in smokers. Addictive Behaviors, 21(4), 481–490[10.1016/0306-4603(95)00070-4]
  16. 16.Norman Hymowitz, Mary Sexton, Judith Ockene, Gregory Grandits. (1991). Baseline factors associated with smoking cessation and relapse. Preventive Medicine, 20(5), 590–601[10.1016/0091-7435(91)90057-b]
  17. 17.Kenneth B. Matheny, Kenneth E. Weatherman. (1998). Predictors of smoking cessation and maintenance. Journal of Clinical Psychology, 54(2), 223–235[10.1002/(sici)1097-4679(199802)54:2<223::aid-jclp12>3.0.co;2-l]
  18. 18.Raymond B. Bridges, Judy G. Combs, Janice W. Humble, John A. Turbek, Stanley R. Rehm, Nancy J. Haley. (1990). Puffing topography as a determinant of smoke exposure. Pharmacology Biochemistry and Behavior, 37(1), 29–39[10.1016/0091-3057(90)90037-i]
  19. 19.Saul Shiffman. (2006). Reflections on smoking relapse research. Drug and Alcohol Review, 25(1), 15–20[10.1080/09595230500459479]
  20. 20.Md. Mahbubur Rahman, Amin Ahsan Ali, Kurt Plarre, Mustafa al'Absi, Emre Ertin, Santosh Kumar. (2011). mConverse. , 1–10[10.1145/2077546.2077557]
  21. 21.Jennifer C. Veilleux, Jon D. Kassel, Adrienne J. Heinz, Ashley Braun, Margaret C. Wardle, Justin Greenstein, Daniel P. Evatt, Megan Conrad. (2010). Predictors and Sequelae of Smoking Topography Over the Course of a Single Cigarette in Adolescent Light Smokers. Journal of Adolescent Health, 48(2), 176–181[10.1016/j.jadohealth.2010.06.015]
  22. 22.Heather Ashton, D. W. Watson. (1970). Puffing Frequency and Nicotine Intake in Cigarette Smokers. BMJ, 3(5724), 679–681[10.1136/bmj.3.5724.679]
  23. 23.Pin Wu, Jun-Wei Hsieh, Jiun-Cheng Cheng, Shyi-Chyi Cheng, Shau-Yin Tseng. (2010). Human Smoking Event Detection Using Visual Interaction Clues. , 4344–4347[10.1109/icpr.2010.1056]
  24. 24.Håvar Brendryen, Pål Kraft, Herman Schaalma. (2010). Looking Inside the Black Box: Using Intervention Mapping to Describe the Development of the Automated Smoking Cessation Intervention ‘Happy Ending’. The Journal of Smoking Cessation, 5(1), 29–56[10.1375/jsc.5.1.29]
  25. 25.Sakari Kolonen, Jouko Tuomisto, Pekka Puustinen, Mauno M. Airaksinen. (1992). Puffing behavior during the smoking of a single cigarette in a naturalistic environment. Pharmacology Biochemistry and Behavior, 41(4), 701–706[10.1016/0091-3057(92)90215-2]
  26. 26.Charles C. Collins, David H. Epstein, Craig S. Parzynski, Debra Zimmerman, Eric T. Moolchan, Stephen J. Heishman. (2009). Puffing behavior during the smoking of a single cigarette in tobacco-dependent adolescents. Nicotine & Tobacco Research, 12(2), 164–167[10.1093/ntr/ntp176]
  27. 27.F. K. Charles, G. R. Krautter, D. C. Mariner. (2009). Post-puff respiration measures on smokers of different tar yield cigarettes. Inhalation Toxicology, 21(8), 712–718[10.1080/08958370802353443]
  28. 28.M L Stitzer, J Gross. (1988). Smoking relapse: the role of pharmacological and behavioral factors.. PubMed, 261, 163–84
  29. 29.(2010). How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease: A Report of the Surgeon General. PsycEXTRA Dataset[10.1037/e590462011-001]
  30. 30.(2008). Treating tobacco use and dependence: 2008 update: Clinical practice guideline. PsycEXTRA Dataset[10.1037/e481882008-001]

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  1. 1.Youfa Wang, Hong Xue, Yaqi Huang, Lili Huang, Dongsong Zhang. (2017). A Systematic Review of Application and Effectiveness of mHealth Interventions for Obesity and Diabetes Treatment and Self-Management. Advances in Nutrition, 8(3), 449–462[10.3945/an.116.014100]
  2. 2.Michael S Businelle, Ping Ma, Darla E Kendzor, Summer G Frank, David W Wetter, Damon J Vidrine. (2016). Using Intensive Longitudinal Data Collected via Mobile Phone to Detect Imminent Lapse in Smokers Undergoing a Scheduled Quit Attempt. Journal of Medical Internet Research, 18(10), e275[10.2196/jmir.6307]
  3. 3.Zachary W. Adams, Erin A. McClure, Kevin M. Gray, Carla Kmett Danielson, Frank A. Treiber, Kenneth J. Ruggiero. (2016). Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research. Journal of Psychiatric Research, 85, 1–14[10.1016/j.jpsychires.2016.10.019]
  4. 4.Andrew L Skinner, Christopher J Stone, Hazel Doughty, Marcus R Munafò. (2018). StopWatch: The Preliminary Evaluation of a Smartwatch-Based System for Passive Detection of Cigarette Smoking. Nicotine & Tobacco Research, 21(2), 257–261[10.1093/ntr/nty008]
  5. 5.Masudul H. Imtiaz, Raul I. Ramos-Garcia, Shashank Wattal, Stephen Tiffany, Edward Sazonov. (2019). Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review. Sensors, 19(21), 4678[10.3390/s19214678]
  6. 6.Rui Fu, Anasua Kundu, Nicholas Mitsakakis, Tara Elton-Marshall, Wei Wang, Sean Hill, Susan J Bondy, Hayley Hamilton, Peter Selby, Robert Schwartz, Michael Oliver Chaiton. (2021). Machine learning applications in tobacco research: a scoping review. Tobacco Control, 32(1), 99–109[10.1136/tobaccocontrol-2020-056438]
  7. 7.Håvar Brendryen, Ayna Johansen, Sverre Nesvåg, Gerjo Kok, Fanny Duckert. (2013). Constructing a Theory- and Evidence-Based Treatment Rationale for Complex eHealth Interventions: Development of an Online Alcohol Intervention Using an Intervention Mapping Approach. JMIR Research Protocols, 2(1), e6[10.2196/resprot.2371]
  8. 8.Volkan Senyurek, Masudul Imtiaz, Prajakta Belsare, Stephen Tiffany, Edward Sazonov. (2019). Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter. Sensors, 19(3), 570[10.3390/s19030570]
  9. 9.Volkan Y. Senyurek, Masudul H. Imtiaz, Prajakta Belsare, Stephen Tiffany, Edward Sazonov. (2020). A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors. Biomedical Engineering Letters, 10(2), 195–203[10.1007/s13534-020-00147-8]
  10. 10.Karl–Heinz Renner, Stephanie Klee, Timo von Oertzen. (2020). Bringing Back the Person into Behavioural Personality Science Using Big Data. European Journal of Personality, 34(5), 670–686[10.1002/per.2303]
  11. 11.Motohiro Nakajima, Andrine M. Lemieux, Mark Fiecas, Soujanya Chatterjee, Hillol Sarker, Nazir Saleheen, Emre Ertin, Santosh Kumar, Mustafa al'Absi. (2020). Using novel mobile sensors to assess stress and smoking lapse. International Journal of Psychophysiology, 158, 411–418[10.1016/j.ijpsycho.2020.11.005]
  12. 12.Arpita Bhattacharya, Samantha Kolovson, Yi-Chen Sung, Mike Eacker, Michael Chen, Sean A. Munson, Julie A. Kientz. (2018). Understanding pivotal experiences in behavior change for the design of technologies for personal wellbeing. Journal of Biomedical Informatics, 79, 129–142[10.1016/j.jbi.2018.01.002]
  13. 13.Zhan Xu, Eline Smit. (2023). Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis. Digital Health, 9, 20552076231183543[10.1177/20552076231183543]
  14. 14.Luis G. Jaimes, Juan Calderon, Juan Lopez, Andrew Raij. (2015). Trends in Mobile Cyber-Physical Systems for health Just-in time interventions. , 28, 1–6[10.1109/secon.2015.7132887]
  15. 15.Alessandro Fenicio, Gaëlle Calvary. (2015). Persuasion Through an Ambient Device: Proof of Concept and Early Evaluation of CRegrette, a Smoking Cessation System. Lecture notes in computer science, 252–267[10.1007/978-3-319-26005-1_17]
  16. 16.Paul Dulaud, Ines Di Loreto, Denis Mottet. (2020). Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles. International Journal of Environmental Research and Public Health, 17(24), 9350[10.3390/ijerph17249350]
  17. 17.Yogendra Patil, Stephen Tiffany, Edward Sazonov. (2014). Understanding smoking behavior using wearable sensors: Relative importance of various sensor modalities. , 2014, 6899–6902[10.1109/embc.2014.6945214]
  18. 18.Masudul H Imtiaz, Volkan Y Senyurek, Prajakta Belsare, Krishna Nagaraja, Edward Sazonov. (2019). Development of a Smart IoT Charger for Wearable Cigarette Smoking Monitor. , 1–5[10.1109/southeastcon42311.2019.9020337]
  19. 19.Mahbubur Rahman, Nasir Ali, Rummana Bari, Nazir Saleheen, Mustafa al’Absi, Emre Ertin, Ashley Kennedy, Kenzie L. Preston, Santosh Kumar. (2017). mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data. , 121–143[10.1007/978-3-319-51394-2_7]
  20. 20.Yang Qin, Weicheng Qian, Narjes Shojaati, Nathaniel Osgood. (2017). Identifying Smoking from Smartphone Sensor Data and Multivariate Hidden Markov Models. Lecture notes in computer science, 230–235[10.1007/978-3-319-60240-0_27]
  21. 21.Swapnil Dhanwal, Vishnu Bhaskar, Tanya Agarwal. (2020). Automated Censoring of Cigarettes in Videos Using Deep Learning Techniques. Asset analytics, 339–348[10.1007/978-981-15-3643-4_26]
  22. 22.Sumeyye Agac, Muhammad Shoaib, Ozlem Durmaz Incel. (2020). Smoking recognition with smartwatch sensors in different postures and impact of user’s height. Journal of Ambient Intelligence and Smart Environments, 12(3), 239–261[10.3233/ais-200558]
  23. 23.Qu Tang. (2014). Automated detection of puffing and smoking with wrist accelerometers. [10.17760/d20018669]
  24. 24.Prajakta Belsare, Volkan Y Senyurek, Masudul H Imtiaz, Stephen T. Tiffany, Edward Sazonov. (2023). DeepPuff: Utilizing Deep Learning for Smoking Behavior Identification in Free-living Environment. , 2023, 1–5[10.1109/embc40787.2023.10340528]
  25. 25.Paul Dulaud, Ines Di Loreto, Denis Mottet. (2020). Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles. Preprints.org[10.20944/preprints202009.0652.v1]
  26. 26.Marah Elfghi, Denise Dunne, Jennifer Jones, Irene Gibson, Gerard Flaherty, John William McEvoy, Sherif Sultan, Fionnuala Jordan, Wael Tawfick. (2021). Mobile health technologies to improve walking distance in people with intermittent claudication. Cochrane Database of Systematic Reviews, 2021(8)[10.1002/14651858.cd014717]
  27. 27.Paul Dulaud, Ines Di Loreto, Denis Mottet. (2020). Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles. Preprints.org[10.20944/preprints202009.0652.v2]
  28. 28.Paul Dulaud, Ines Di Loreto, Denis Mottet. (2020). Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles. Preprints.org[10.20944/preprints202009.0652.v3]
  29. 29.Vesna Zeljković, Junjia Zhu, Joshua E. Muscat. (2026). New Approaches to Analyzing Data from Smoke Topography Devices: A Pilot Analysis. , 1–5[10.1109/gmepe/pahce69420.2026.11550339]
  30. 30.Zhiwei Zhang, Xiaobo Lu. (2024). YOLO-MSP: Effective Cigarette Detection YOLO Network for Surveillance Scenario. , 1–8[10.1109/icsai65059.2024.10893757]
  31. 31.Rosalie E. Ruhlmann, Catharine E. Fairbairn, Julianne C. Flanagan. (2023). Novel methods in addiction treatment: Advances in telehealth, neuromodulation, and mobile interventions for substance use disorder. ˜The œPsychology of learning and motivation/˜The œpsychology of learning and motivation, 243–270[10.1016/bs.plm.2023.06.001]
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