Motion-Robust Multimodal Heart Rate Estimation Using BCG Fused Remote-PPG With Deep Facial ROI Tracker and Pose Constrained Kalman Filter

The heart rate (HR) signal is so weak in remote photoplethysmography (rPPG) and ballistocardiogram (BCG) that HR estimation is very sensitive to face and body motion disturbance caused by spontaneous head and body movements as well as facial expressions of subjects in conversation. This article proposed a novel multimodal quasi-contactless HR sensor to ensure the robustness and accuracy of HR estimation under extreme facial poses, large-motion disturbances, and multiple faces in a video for computer-aided police interrogation. Specifically, we propose a novel landmark-based approach for a deep facial region of interest (ROI) tracker and face pose constrained Kalman filter to continuously and robustly track target facial ROIs for estimating HR from face and head motion disturbances in rPPG. This motion-disturbed rPPG signal is further fused with a minimally disturbed BCG signal by the face and head movements via a bank of notch filters with a recursive weighting scheme to obtain the dominant HR frequency for final accurate HR estimation. To facilitate reproducible HR estimation research, we synchronously acquire and publicly share a multimodal data set that contains 20 sets of ECG and BCG signals as well as uncompressed, rPPG-dedicated videos from ten subjects in a stable state and large-motion state (MS) without and with large face and body movements in a sitting position. We demonstrate through experimental comparisons that the proposed multimodal HR sensor is more robust and accurate than the state-of-the-art single-modal HR sensor solely with rPPG- or BCG-based methods. The mean absolute error (MAE) of HR estimation is 7.13 BPM lower than the BCG algorithm and 3.12 BPM lower than the model-based plane-orthogonal-to-skin (POS) algorithm in the MS.