Fusion of ballistocardiography and imaging for improved non-contact heart rate monitoring

Camera based imaging is the most widely used technique for non-contact heart rate monitoring (HRM). However, it's robustness to motion artifacts and the absence of reliability when the patient's face is not in the camera's field of view still persists. This is often addressed with computationally heavy AI algorithms or hardware intensive multi-camera systems. Here, we investigate the improvement in accuracy and reliability of noncontact HRM by augmenting vision with an unobtrusive near field sensing modality such as ballistocardiography. The system seamlessly transitions between the two modalities based on a real time signal quality index (SQI). The SQI parameter was able to discard segments of higher errors and select the data stream with lower error. The proposed system was validated on data collected from 20 subjects, with induced motion and facial occlusions to create artifacts. The accuracy was validated against a contact-based ground truth reference. The findings indicate a notable enhancement in the temporal coverage upto 100 percent. The proposed method had a competent error ranging between 3 to 17 bpm, with a median error of 8 bpm.