Weakly Supervised Learning for Camera-Based Heart Rate Variability

Camera-based pulse measurements from remote photoplethysmography (rPPG) have rapidly improved over recent years due to innovations in video processing and deep learning. However, modern data-driven solutions require large training datasets collected under diverse conditions. Collecting such training data is made more challenging by the need for time-synchronized video and physiological signals as ground truth. This paper presents a weakly supervised learning framework, Freq2Time, to train with heart rate (HR) labels. Our framework mitigates the need for simultaneous PPG or ECG as ground truth, since the HR changes relatively slowly and describes the target rPPG signal over a time interval. We show that 3D convolutional neural network (3DCNN) models trained with the Freq2Time framework give state-of-the-art HR performance with MAE of 2.86 bpm, when tested with challenging smartphone video data from 30 subjects. Additionally, our models still learn accurate rPPG time signals, allowing for other physiological metrics such as heart rate variability.