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

Summary

A thesis (not a peer-reviewed venue paper) building a two-layer wearable HR system for a custom finger-ring PPG platform (τ-Ring, 100 Hz IR/red + accelerometer): an estimation layer that generates ~140 candidate HR values per window from multiple DSP estimators/channels/harmonics, scores them with a small MLP, and decodes a temporally-consistent path with causal Viterbi (HMM-style dynamic programming, past-only); and a reporting layer that estimates reliability and applies a learned 3-way policy — accept the new estimate, hold the last trusted value, or reject the window outright. Directly and independently replicates the "selective reporting reduces motion-window MAE substantially" finding on an external wrist-BVP cohort: PPG-DaLiA, with proper subject-disjoint LOSO, no ring-trained weights carried over.