VitalLens 2.0 用大模型和新数据，从人脸视频精准测心率变异性。
VitalLens 2.0: High-Fidelity rPPG for Heart Rate Variability Estimation from Face Video
- 采用新架构+1413人多样数据训练，提升信号还原精度。
- 心率误差仅1.57 bpm，HRV指标误差低于17毫秒，创最优表现。
- 适合医疗健康、可穿戴设备开发者快速接入使用。
本文介绍VitalLens 2.0，一种用于从面部视频中估计生理信号的深度学习模型。该模型在远程光电容积脉搏波描记术（rPPG）方面实现显著精度提升，可鲁棒地估计心率（HR）、呼吸率（RR）及心率变异性（HRV）指标。这一进展得益于新模型架构与训练数据规模和多样性的大幅增加，总计覆盖1,413名不同个体。我们在包含4个公开和私有数据集的422名个体的新联合测试集上进行评估。按个体平均结果，VitalLens 2.0在心率估计上的均方绝对误差（MAE）为1.57 bpm，呼吸率误差为1.08 bpm，HRV-SDNN误差为10.18毫秒，HRV-RMSSD误差为16.45毫秒。这些结果达到当前最优水平，显著优于此前方法。该模型现已通过VitalLens API向开发者开放，地址为https://rouast.com/api。
原文摘要 · Abstract (English)
This report introduces VitalLens 2.0, a new deep learning model for estimating physiological signals from face video. This new model demonstrates a significant leap in accuracy for remote photoplethysmography (rPPG), enabling the robust estimation of not only heart rate (HR) and respiratory rate (RR) but also Heart Rate Variability (HRV) metrics. This advance is achieved through a combination of a new model architecture and a substantial increase in the size and diversity of our training data, now totaling 1,413 unique individuals. We evaluate VitalLens 2.0 on a new, combined test set of 422 unique individuals from four public and private datasets. When averaging results by individual, VitalLens 2.0 achieves a Mean Absolute Error (MAE) of 1.57 bpm for HR, 1.08 bpm for RR, 10.18 ms for HRV-SDNN, and 16.45 ms for HRV-RMSSD. These results represent a new state-of-the-art, significantly outperforming previous methods. This model is now available to developers via the VitalLens API at https://rouast.com/api.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。