CITRUS：用于可靠、非侵入式感知运动状态下可穿戴心率的候选推断与时间跟踪
CITRUS: Candidate Inference and Temporal-tracking for Reliable, Unobtrusive Sensing of Wearable Heart Rate under Motion
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中文总结 AI 辅助
研究针对运动干扰下可穿戴心率估计问题，提出CITRUS系统，通过候选频率识别、时间解码和可靠性感知报告三个阶段，利用两层结构，减少运动MAE，估计器参数少且计算预算低，在不同队列实验中取得较好效果。
中文摘要 AI 辅助
可穿戴光电容积脉搏波描记法（PPG）能提供连续心率测量，但运动时其准确性会下降。在环形平台基准测试中，最佳监督基线在整体心率任务上的平均绝对误差（MAE）达到5.33次/分钟。在仅关注运动的环形审核中，监督长短期记忆网络（LSTM）基线在运动窗口上的MAE为$14.39 \pm 0.47$次/分钟，简单平滑和ACC先验仅将其降至$13.00 \pm 0.41$次/分钟。本文通过候选频率识别、时间解码和可靠性感知报告三个相连阶段解决运动干扰下的心率估计问题。首先评估了两种可穿戴PPG环变体与54名参与者的心率、呼吸、血氧饱和度和血压的临床参考值。所提系统使用两层结构，估计层将每个窗口转换为约140个频率候选值，通过独立估计器之间的一致性对候选值评分，并应用带生理转换惩罚的因果维特比解码。报告层估计可靠性并应用学习到的接受/保留/拒绝策略。仅报告最有信心的50%的运动窗口可将运动MAE从10.8降至6.2次/分钟。心率估计器使用少于10万个参数，在微控制器级计算预算内运行。额外的PPG - DaLiA实验在独立的手腕血容量脉搏（BVP）队列上评估了相同的候选选择和时间解码估计器。
英文摘要
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.
