科学依据
实验室验证，绝非广告噱头。
该扫描仪基于 rPPG（远程光电容积脉搏波描记法），结合 AI、计算机视觉和机器学习模型，在设备神经引擎上运行，将普通视频转化为生命体征数据。这一方法起源于某大学激光实验室，在 MIT 深化成熟，并在全球医院患者中得到验证。以下是附有具体数据的真实研究成果，供您自行核查。
33 篇同行评审研究涵盖 2008 年至 2026 年3 项 FDA 认证已与 ECG 对比验证
技术来源
诞生于全球顶尖研究实验室。
UC Irvine, Beckman Laser Institute
2008 年发现：普通摄像头可在数米外从皮肤中读取脉搏信号。
MIT Media Lab
将这一发现转化为基于摄像头的心率、呼吸率与 HRV 测量，并经医疗传感器验证。
University of Oxford
证明摄像头生命体征监测在重症监护室及新生儿病房的真实患者中同样有效。
Philips Research & TU Eindhoven
构建了抗运动干扰的 CHROM 与 POS 算法，确保运动状态下的测量准确性。
Microsoft Research
率先推出深度学习模型，将摄像头测量精度逼近专用传感器水平。
Tsinghua, Oulu, USTB, HKUST
持续推进 Transformer 与状态空间模型，年复一年刷新精度记录。
什么是 rPPG？
脉搏改变皮肤颜色。
每次心跳时，一股血流会穿过皮肤浅层血管。这些血液会吸收更多绿光，因此每次心跳都会使皮肤产生肉眼难以察觉的明暗变化。
肉眼无法捕捉，但摄像头可以，AI 能够读取。光电容积脉搏波描记法（PPG）与医院指夹式脉搏传感器或智能手表所采用的光学方法相同。远程 PPG 无需任何接触，仅凭面部视频即可在一定距离外完成测量。
血容量脉搏波
AI 从单段面部视频中还原出该波形，并从中解读出数十项生命体征。
扫描原理
从摄像头到生物标志物。
1
采集
前置RGB摄像头以高帧率记录面部皮肤颜色的细微变化，并通过环境光传感器对曝光进行归一化处理。
2
追踪
计算机视觉借助红外深度感应，精准锁定数百个面部特征点，分离出干净的皮肤区域，同时过滤运动与光线噪声。
3
提取
信号处理与机器学习模型从原始颜色轨迹中还原血容量脉冲（BVP波形）。
4
测量
设备神经引擎上的AI模型读取脉冲中的峰值与节律，输出心率、心率变异性、呼吸频率等多项指标。
研究依据
公开透明的研究成果。
按类型分组并标注可信度级别。心率、心率变异性和呼吸频率已得到充分验证，血氧饱和度（SpO2）正处于研究验证阶段，血压仍属实验性指标。每张卡片均附有原始资料链接。
基础研究与前沿探索
从 2008 年的技术发现，到如今的 Transformer 与状态空间模型。这一领域每年持续突破。
临床验证
已在医院、重症监护室、远程医疗及诊所的真实患者中完成测试，涵盖2024年至2026年的研究。
综述、荟萃分析与基准测试
最高级别的循证依据：多项研究汇总评估，并附开放基准测试与公平性审计。
监管认可
已获FDA批准，应用于真实产品。
基于摄像头的生命体征测量并非仅停留于学术层面。监管机构已批准商业rPPG设备用于脉率和呼吸频率的测量。
坦诚面对局限性
- 面部扫描生命体征是用于追踪趋势的健康工具，不构成诊断，也不能替代专业医疗服务。
- 心率、心率变异性和呼吸频率的准确性最高；血氧检测尚在发展阶段，血压检测仍处于实验性阶段。
- 光线、动作和摄像头质量会影响结果。光线充足均匀、保持静止，可获得最佳读数。
- 我们不声称能以临床事实的方式测量血糖、胆固醇或血液化学指标。对于尚处于早期阶段的科学研究，我们将其标注为"新兴"。
参考文献
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将循证扫描技术引入您的业务。
科学依据已足够充分，可供构建应用。如需将扫描仪集成至您的产品，我们将负责所有技术实现。
Plondo rPPG 扫描仪本身未获得 FDA 许可。所提及的 FDA 许可是指底层 rPPG 技术已在其他公司的商业设备中获得许可。它是一款用于追踪健康趋势的健康管理工具，并非医疗器械，不用于诊断、治疗、治愈或预防任何疾病。