CITRUS: Candidate Inference and Temporal-tracking for Reliable, Unobtrusive Sensing of Wearable Heart Rate under Motion

Abstract

Wearable photoplethysmography (PPG) provides continuous heart-rate measurements, but its accuracy degrades under motion. In the ring-platform benchmark, the best supervised baseline reaches 5.33 BPM mean absolute error (MAE) on the overall heart-rate task. In the motion-focused ring-only audit, a supervised LSTM baseline reaches $14.39 \pm 0.47$ BPM MAE on motion windows, and simple smoothing and ACC priors reduce this only to $13.00 \pm 0.41$ BPM. This thesis addresses motion-corrupted HR estimation through three connected stages: candidate-frequency identification, temporal decoding, and reliability-aware reporting. The study first evaluates two wearable PPG ring variants against clinical references for heart rate, respiration, SpO$_2$, and blood pressure in 54 participants. The proposed system then uses two layers. The estimation layer converts each window into approximately 140 frequency candidates, scores candidates using agreement among independent estimators, and applies causal Viterbi decoding with a physiological transition penalty. The reporting layer estimates reliability and applies a learned accept/hold/reject policy. Reporting only the most-confident 50% of motion windows reduces motion MAE from 10.8 to 6.2 BPM. The heart-rate estimator uses fewer than 100k parameters and runs within a microcontroller-class compute budget. Additional PPG-DaLiA experiments evaluate the same candidate-selection and temporal-decoding estimator on an independent wrist-BVP cohort.