DISSERTATION DEFENSE

Author : Andrew Kenneth Smith 
Advisors: Dr. Homayoun Valafar
Date: October 21, 2026
Time: 02:00 pm
Location: AI Seminar Room 529
Link:  https://teams.microsoft.com/meet/226969496196462?p=FsccssHidNdPu1F1S5

Abstract

Physiological signals such as EEG, accelerometry, and inertial motion carry rich information about health and behavior, yet building reliable classifiers that work outside the laboratory remains difficult. This dissertation advances a single thesis: machine learning classifiers for physiological time series generalize well across health behaviors, clinical contexts, and species when sufficient population data is available, but population models do not generalize uniformly to the individual. Closing the gap to the individual requires adapting the model itself: recalibrating a per-person decision threshold is a cheap, label-light first step, but it recovers only part of what fine-tuning achieves. How much it recovers depends on the person as well as the behavior, so the regimes that decide the right intervention (calibration-limited, representation-limited, and individual-invariant) belong to a person and behavior pair rather than to a behavior, and the central contribution is a framework that identifies which regime a person and behavior pair falls in and matches the personalization intervention accordingly.

The argument is developed through five studies that progress from population models toward personalization. A feature-engineering baseline establishes classical machine learning for rodent sleep staging, after which a deep convolutional recurrent model reproduces expert-level vigilance-state scoring at a macro F1 of 0.878. The same architectural principles transfer to human activity recognition, where a single model discriminates multiple health behaviors from a single wrist sensor at a macro F1 of 0.817, and to medication-adherence monitoring in opioid use disorder treatment, where neural gesture recognition detects therapeutic self-administration. A capstone study on smoking detection exposes the core obstacle directly: population models cluster gestures cleanly yet classify individuals only moderately. Recalibrating each participant’s decision threshold, with zero gradient updates, raises mean smoking-class F1 from 0.702 to 0.767, but this recovers only 43.1% of the gain that fine-tuning achieves (F1 0.853), and fine-tuning beats calibration significantly. The convolutional layers can stay frozen, but the recurrent layers must adapt, and for one participant calibration recovers almost nothing and only fine-tuning closes the gap. In the medication-adherence setting of opioid use disorder, distinguishing methadone from buprenorphine is representation-limited at the behavior level: threshold recalibration does not help in any of three runs, while fine-tuning does. Personalization therefore needs model adaptation, with calibration as its cheap first step, and whether that first step suffices must be judged for each person and behavior pair, not per behavior alone.

Alongside the empirical studies, the dissertation contributes a reproducible annotation and experimentation platform that supports the full pipeline from raw signal import to labeled datasets and trained models. Taken together, the studies show that the path from accurate population-level classification to deployable personalized health monitoring runs through diagnosing which regime an individual’s behavior falls in and matching the personalization intervention to that regime, not through ever-larger models, and they chart a practical route for that transition across diverse physiological sensing domains.