无信号时的Oracle余量：热心率估计候选选择的零校准评估
Oracle headroom without signal: null-calibrated evaluation of candidate selection for thermal heart rate estimation
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中文总结 AI 辅助
针对热心率估计中Oracle评估的偶然一致性偏差，提出用顺序统计量建模并建议报告候选数、覆盖率和零对照，以校准评估结果。
中文摘要 AI 辅助
基于摄像头的生理监测可以从多个面部区域、提取方法和处理设置中产生多种估计。信号质量指数旨在在没有生理参考的情况下选择可靠的估计，其潜力通常通过一个Oracle来评估，该Oracle在每个窗口中选择最接近参考的估计。这种回顾性选择可能会奖励偶然的一致性。我们用顺序统计量来建模这种效应。对于与参考无关的K个独立候选，预期的Oracle误差大约按1/K减少。我们分析了96个iBVP记录上的热心率估计，每个10秒窗口有168个候选。Oracle实现了0.91 bpm的平均绝对误差，而最佳固定配置为10.74 bpm，最佳质量指数为18.03 bpm，常数预测器为8.61 bpm。当K=24时，来自另一个记录的额头信号与正确信号匹配，误差为4.62对4.61 bpm。Oracle评估应报告候选数量、有效覆盖率和匹配的零对照。
英文摘要
Camera-based physiological monitoring can produce multiple estimates from several facial regions, extraction methods, and processing settings. Signal quality indices aim to select reliable estimates without a physiological reference, and their potential is often assessed with an oracle that selects the estimate closest to the reference in each window. This retrospective selection can reward chance agreement. We model the effect with order statistics. For K independent candidates unrelated to the reference, the expected oracle error decreases approximately as 1/K. We analyze thermal heart rate estimation on 96 iBVP recordings with 168 candidates per 10 s window. The oracle achieves a mean absolute error of 0.91 bpm, compared with 10.74 bpm for the best fixed configuration, 18.03 bpm for the best quality index, and 8.61 bpm for a constant predictor. With K = 24, a forehead signal from another recording matches the correct one, with 4.62 against 4.61 bpm. Oracle evaluations should report candidate count, valid coverage, and matched null controls.
发表机构
- Center for Machine Vision and Signal Analysis (CMVS), University of Oulu（奥卢大学机器视觉与信号分析中心）
机构由 AI 辅助整理，请以论文原文为准。
