高海拔下用多摄像头视频测生命体征，融合面部与指尖数据提升精度。
Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes
- 融合面部和指尖视频信号，提升血氧与心率估计精度。
- 跨人体评估中血氧误差低于2.5%，心率误差小于0.5 BPM。
- 同时训练多个生理指标可使血氧预测误差降低17.8%。
视频光体积描记法（vPPG）是一种新兴的非侵入式生理信号测量方法，主要分为远程视频PPG（rPPG）和接触式视频PPG（cPPG）。在高海拔环境中，心率常升高而血氧水平下降，监测生命体征面临挑战。为此，我们提出了SUMS数据集，包含10名受试者在运动与吸氧恢复阶段采集的80组同步非接触面部与接触指尖视频，涵盖PPG、呼吸频率（RR）和SpO2。该数据集用于验证视频生命体征估计算法，并对比面部rPPG与指尖cPPG的表现。结果显示，融合不同位置视频可使SpO2预测的平均绝对误差（MAE）分别比仅用面部和仅用指尖降低7.6%和10.6%。在跨主体评估中，心率估计的MAE低于0.5 BPM，SpO2估计的MAE为2.5%，表明多摄像头融合技术具有高精度。此外，同时训练多个指标（如PPG与血氧）可使SpO2估计的MAE降低17.8%。
原文摘要 · Abstract (English)
Video photoplethysmography (vPPG) is an emerging method for non-invasive and convenient measurement of physiological signals, utilizing two primary approaches: remote video PPG (rPPG) and contact video PPG (cPPG). Monitoring vitals in high-altitude environments, where heart rates tend to increase and blood oxygen levels often decrease, presents significant challenges. To address these issues, we introduce the SUMS dataset comprising 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios, capturing PPG, respiration rate (RR), and SpO2. This dataset is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Additionally, fusing videos from different positions (i.e., face and finger) reduces the mean absolute error (MAE) of SpO2 predictions by 7.6\% and 10.6\% compared to only face and only finger, respectively. In cross-subject evaluation, we achieve an MAE of less than 0.5 BPM for HR estimation and 2.5\% for SpO2 estimation, demonstrating the precision of our multi-camera fusion techniques. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO2 estimation by 17.8\%.
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