mPuff: Automated Detection of Cigarette Smoking Puffs from Respiration Measurements Amin Ahsan Ali, Syed Monowar Hossain, Karen Hovsepian, Md. Mahbubur Rahman, Kurt Plarre, and Santosh Kumar University of Memphis Puff detection IPSN 2012, Beijing, China

2 Acknowledgements Saul Shiffman Psychology University of Pittsburgh Mustafa al’Absi Behavioral Science University of Minnesota Emre Ertin Electrical & Computer Engineering The Ohio State University

Causes cancer in different organs throughout the body leads to cardiovascular and respiratory diseases harms reproduction 3 Smoking Kills In US alone Tobacco is the cause for one of every five deaths Smokers die years younger Public health burden of $193 billion annually Ref: Mukdad A. H., et al., Actual Causes of Death in the United States, 2000, JAMA 2004

The 6 month quit rate was 3-5% in 5 of the 6 studies Ref: Hughes J.R.,et. al., Shape of the relapse curve and long-term abstinence among untreated smokers, Hughes,, Addiction, 99(1),pp. 29—38, ’04. 4 Self-Quitting is NOT an Option survival curve relapse curve self-quitters control groups in cessation programs

Eight (out of 27) divisions at NIH award research grants for smoking cessation programs NIH alone awards $350+ million annually in smoking research 1 Still, smoking continues to be prevalent – Each day about 3,000 people become new daily smokers – Success rate of most smoking cessation programs is less than 10% The reason seems to be – Most research are self report based that introduces bias – NO reliable method to detect smoking and intervene at the right moment 5 Research in Smoking [1] Estimates of Funding for Various Research, Condition, and Disease Categories (RCDC), NIH (

6 Current State of the Art Devices are available that measure and display/store CO levels in a single breath exhaled through a mouthpiece attached to them piCO+/Micro+ are designed for use as motivational aid CReSS is used to observe smoking patterns and the degree of tobacco intake Requires compliance from the users May cause embarrassment using in front of others piCO+ and Micro+ CReSS Pocket DOES NOT DETECT SMOKING & CANNOT PROVIDE REAL TIME INTERVENTIONS

AutoSense System for Data Capture in Field Using AutoSense System, Plarre et. al., showed that stress can be detected reliably from respiration measurements. (IPSN’11 2 ) 2 Plarre et. al., "Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment”, IPSN, 2011 Chestband sensors: ECG, Respiration, GSR, Ambient & Skin Temp., Accelerometer Armband sensors: Alcohol (WrisTAS), Temp., GSR, Accelerometer Wireless Android G1 Smart Phone Continuous Assessment of Physiology, Stress, and Addictive Behaviors in Field Details about AutoSense is available in an ACM Sensys’11 1 Paper 1 Ertin et. al., "AutoSense: Unobtrusively Wearable Sensor Suite for Inferencing of Onset, Causality, and Consequences of Stress in the Field,“ SenSys, 2011.

8 Memphis Study – 40 daily smokers and social drinkers – One week of AutoSense wearing in the field Stress, drinking, smoking, and craving for cigarettes are reported National Institute on Drug Abuse (NIDA) Study – 20 drug users undergoing treatment – Two lab sessions and 4 weeks of wearing AutoSense in the field Smoking, craving, and stress events are marked in the lab Craving, stress, and drug usage are reported in the field Johns Hopkins Study – 10 drug users in residential treatment – Drug self-administration sessions are marked in the lab Ongoing Field Studies with AutoSense

9 ItemMemphis StudyNIDA Study # of participants completed203 # of person days worth of data140 days34 days Amount of good quality sensor data76,312 min22,125 min # hours worth of data1,272 hours369 hours Avg data collection per day9 hours/day10.8 hours/day # of EMA received2145 (or 16/day)253 (7.5/day) % of EMA answered94%91.3% # of smoking self-report953 (or 6.8/day)116 (or 3.4/day) # of drinking self-report101 (5.6/week)--- # of craving self-report---10 # of drug used self-report---6 Data Collection Statistics

Inference of Conversation and Stress from respiration is shown to be possible 1,2 – Most smokers smoke during conversations and with other smokers – Stress has been found to be a predictor of smoking 10 Inferences from Respiration Real time detection of smoking opens up the opportunity for Analysis of contexts of smoking Finding true predictors of smoking [1] Md. Mahbubur Rahman, Amin Ahsan Ali,et. al., "mConverse: Inferring Conversation Episodes from Respiratory Measurements Collected in the Field," In Proceedings of ACM Wireless Health, San Diego, CA [2] K. Plarre, A. Raij, et. al., "Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment," In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks ( IPSN), Chicago, IL, 2011.

11 Respiration Chest Band Concept of mPuff Smoking puff detection Cell Phone Captures Signal

12 Show puff detection is possible from respiration Open up research on automated smoking detection Identify several new respiration features Key Contributions

13 System Overview Features Computed from each cycle SVM Classifier Trained using carefully labeled data Semi-Supervised Classifier Utilizing the data collected in natural environment

14 Inhalation Duration Exhalation DurationStretch Features IERatio = Inhalation Duration / Exhalation Duration Respiration Duration = Inhalation + Exhalation Duration

15 Respiration during Smoking PUFFS Compared to Non-puff cycles PUFFs have: HIGH Stretch Similar upper & lower parts Relative to neighboring cycles significant change in stretch and other features

Feature Statistics 16 New Features(1) Stress Speaking Smoking Unlike Conversation or Stress, SMOKING PUFF Cycles have LONGER Stretch Symmetric across the mid- axis Significant relative change in stretch and other features

New Features (2) Smoking Running Unlike Running SMOKING PUFFS Cycles Significant relative change in stretch and Exhalation Duration

1 st Difference of Inhalation, Exhalation durations, IE ratio, Stretch 18 New Features TOTAL OF 12 New Features Ratio of Stretch (Exhalation Duration) of a cycle, C to the avg. across neighboring cycles We considered a window of 5 cycles centered around C Upper (Lower) Stretch value – taking the difference of peak(valley) amplitude from the running mean of valley amplitudes

Classification Support Vector Machines are used as classifier – Classify respiration cycles to Puffs and Non-puffs – Puff data obtained from 10 participants 13 sessions – Non-puffs come from smoking sessions Stress data Conversation data Physical Activity data Area Under ROC Curve (AUC) metric to assess the performance – because we have highly imbalanced class sizes. 19 Classification

20 Training

21 Testing

Unlabeled data collected from 4 participants – They provide self-reports of smoking episodes – May not report at the beginning of smoking episodes Distance to self report is added as a feature An S3VM is employed – Increases accuracy to 87% 22 Semi-Supervised Classifier

Limitations & Future Work Duration of smoking session: 6.62 ± 1.66 minutes Puff duration: 1.09 ± 0.53 seconds Inter-puff interval: ± seconds Number of puffs/smoking session: ± 0.92 Consistent with previous lab and field based topology studies 23 Smoking Topology

Performance of classifier in different confounding events 24 Limitations

Conclusion Novelty – First system to show smoking puff detection is possible from respiration which can enable scientific studies on How stress level can predict smoking behavior How conversation is related to smoking behavior New predictors of smoking – Identifies several new discriminatory features from respiration which may help in detecting other states, e.g., Eating, and drinking from respiration Ongoing Work – Use correlated contexts and robust features to develop an automated smoking session detector Address the system reliability and efficiency issues 25 Conclusions

AutoSense Papers 26 Further Reading [System] E. Ertin, N. Stohs, S. Kumar, A. Raij, M. al'Absi, T.Kwon, S. Mitra, Siddharth Shah, and J. W. Jeong, “AutoSense: Unobtrusively Wearable Sensor Suite for Inferencing of Onset, Causality, and Consequences of Stress in the Field,” ACM SenSys, [Conversation] M. Rahman, A. Ahsan Ali, K. Plarre, M. al'Absi, E. Ertin, and S. Kumar, “mConverse: Inferring Conversation Episodes from Respiratory Measurements Collected in the Field,” ACM Wireless Health, [Incentive] M. Mustang, A. Raij, D. Ganesan, S. Kumar and S. Shiffman, “Exploring Micro-Incentive Strategies for Participant Compensation in High Burden Studies,” to appear in ACM UbiComp, [Stress] K. Plarre, A. Raij, M. Hossain, A. Ali, M. Nakajima, M. al'Absi, E. Ertin, T. Kamarck, S. Kumar, M. Scott, D. Siewiorek, A. Smailagic, and L. Wittmers, “Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment,” ACM IPSN, [Privacy] A. Raij, A. Ghosh, S. Kumar and M. Srivastava, “Privacy Risks Emerging from the Adoption of Inoccuous Wearable Sensors in the Mobile Environment,” In ACM CHI, 2011.