THESIS DEFENSE

Author :  Jakub Jerzmanowski
Advisors: Dr. Homayoun Valafar
Date: July 10, 2026
Time: 10:00 Am
Location:  Room 2265, Storey Innovation building

Link:  https://teams.microsoft.com/l/meetup-join/19%3ameeting_NWQzZDZhOTItMjNi…

Abstract
Cigarette smoking remains the leading cause of preventable death in the United States, claiming approximately 480,000 lives annually. Clinicians who tailor cessation interventions are limited by their data: knowing that a person smoked is far less useful than knowing when each puff began and ended, information from which count, duration, and inter-puff interval follow. Wrist-worn inertial measurement unit (IMU) systems can sense smoking unobtrusively, but existing methods classify coarse fixed-length windows rather than the puffs themselves, and so cannot recover this structure. We frame puff detection as dense, per-timestep segmentation, labeling every IMU sample and evaluating at the event level. To our knowledge, this is the first treatment of smoking-puff detection as a segmentation task. Using leave-one-subject-out cross validation, we show that window classifiers, run densely, collapse under strict overlap (event F1 at IoU 0.75 near 0.05), placing predictions in the right neighborhood but the wrong shape, whereas a 1D U-Net adapted to the time domain reaches 0.714 on our 1,500-hour, six-participant in-situ dataset. Decomposing the residual error shows that localization is essentially solved (matched-puff IoU of 0.93 to 0.99); the entire remainder is missed detections, concentrated in one hard participant who drives a pooled miss rate of 0.101. Because the bottleneck is per-person recall, we close it with personalization: warm restarting on as few as ten of a participant’s own puffs cuts the hard subject’s miss rate from 0.38 to 0.05 while boundary quality holds, reframing the problem as one of data and personalization rather than architecture.