Remote HeartRate Measurement based on Signal Feature Detection in Time Domain

Remote-photoplethysmography (rPPG) is a contactless method which can measure Blood Volume Pulse (BVP) and several vital signals from human faces. Until now, most existing techniques depend on frequency analysis and regard the signal component with largest energy as Heart Rate (HR). Nonetheless, the property, that frequency analysis could not identify the instantaneous HR changes restricts the applications on Heart Rate Variability (HRV) analysis and Cardiac Arrhythmia Detection. Consequently, this paper proposes a time domain systolic peak selection algorithm. It reaches 89% Suc5 in stationary case and 90% Suc10 in indoor scenario which are neck and neck with frequency domain analysis. At the same time, the comparison method, Rei-En, gets Suc10 lower than 80% in both situations. We investigate the influence of dicrotic notch and utilize a concise method to prevent wrong Inter-beat interval (IBI) detection for HR and HRV estimation. Benefiting from the characteristic of time domain analysis, the first HR output could be estimated within five seconds, which is much shorter than seventeen seconds in frequency domain analysis. The results suggest that the proposed algorithm improves instantaneity and provides faster first output of rPPG in HR, HRV estimation.