Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts. Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals. Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals. Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion. Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.

中文翻译：

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HRVCam：基于相机的强大心率变异性测量

意义：非接触式、基于相机的心率变异性估计在许多应用中是可取的，包括医疗、汽车和娱乐。不幸的是，基于相机的 HRV 准确性和可靠性受到两个挑战的影响：(a) 较暗的肤色导致较低的 SNR 和 (b) 相对运动会导致测量伪影。目的：我们提出了一种 HRVCam 算法，该算法对低信噪比和运动诱发伪影通常存在于成像光电容积描记 (iPPG) 信号中提供足够的鲁棒性。方法：HRVCam 根据 iPPG 信号的瞬时频率计算基于相机的 HRV。HRVCam 使用自动自适应带宽滤波和离散能量分离来估计瞬时频率。HRVCam 的参数使用 HRV 和 iPPG 信号的观察特征。结果：我们捕获了一个包含 16 名具有不同肤色的参与者的新数据集。我们证明 HRVCam 显着减少了基于相机的 HRV 指标的误差（减少了 50% 以上），用于具有深色皮肤和面部运动的视频。结论：HRVCam 可用于 iPPG 估计算法之上，以提供稳健的 HRV 测量，使基于相机的 HRV 变得实用。