UC San Diego

The sensors required to mimic clinical metrics of anything from pupil change to blood pressure to functional strength are already present in the smartphones that billions of people carry in their pockets. The barrier to using them for clinical measurement is not hardware, it is software. This dissertation presents research developing and validating smartphone-based sensing systems across three clinical domains: neurological assessment, cardiovascular monitoring, and musculoskeletal assessment, with two paired goals: expanding access to established clinical measurements and facilitating the discovery of novel digital biomarkers. In neurological assessment, two smartphone pupillometry systems are presented, one repurposing the near-infrared facial recognition camera for absolute pupil diameter measurement (median MAE 0.27 mm against a clinical pupillometer) and one using far-red optical filtering for universal RGB camera compatibility (mean Pearson correlation 0.93, MAE 2.4%), with both systems applied toward mild cognitive impairment screening via pupillary response features. In cardiovascular monitoring, the first absolute oscillometric blood pressure measurement on an unmodified smartphone is achieved through Vibrometric Force Estimation (VFE), which uses the built-in vibration motor and inertial measurement unit to measure fingertip force without dedicated hardware, yielding systolic MAE of 9.21 mmHg and AUC of 0.92 for hypertension screening. In musculoskeletal assessment, inverse kinematics features extracted from inertial measurement unit data during the 30-second chair stand test substantially outperform the conventional repetition count for body composition prediction, achieving Pearson correlations of 0.94 for total lean mass and 0.73 for percent body fat against dual-energy X-ray absorptiometry in 80 participants. Across all three domains, systems are anchored to universally available smartphone sensors, validated against clinical gold standards, and evaluated for demographic equity, demonstrating that clinically relevant measurement and biomarker discovery are achievable through software repurposing of hardware that already exists in consumers' hands.