Automatic webcam-based human heart rate measurements using laplacian eigenmap

Non-contact, long-term monitoring human heart rate is of great importance to home health care. Recent studies show that Photoplethysmography (PPG) can provide a means of heart rate measurement by detecting blood volume pulse (BVP) in human face. However, most of existing methods use linear analysis method to uncover the underlying BVP, which may be not quite adequate for physiological signals. They also lack rigorous mathematical and physiological models for the subsequent heart rate calculation. In this paper, we present a novel webcam-based heart rate measurement method using Laplacian Eigenmap (LE). Usually, the webcam captures the PPG signal mixed with other sources of fluctuations in light. Thus exactly separating the PPG signal from the collected data is crucial for heart rate measurement. In our method, more accurate BVP can be extracted by applying LE to efficiently discover the embedding ties of PPG with the nonlinear mixed data. We also operate effective data filtering on BVP and get heart rate based on the calculation of interbeat intervals (IBIs). Experimental results show that LE obtains higher degrees of agreement with measurements using finger blood oximetry than Independent Component Analysis (ICA), Principal Component Analysis (PCA) and other five alternative methods. Moreover, filtering and processing on IBIs are proved to increase the measuring accuracy in experiments.