Video-Based Heartbeat Rate Measuring Method Using Ballistocardiography

• Published in: IEEE sensors journal 2017-07, Vol.17 (14), p.4544-4557
• Main Authors: Hassan, Mohamed Abul, Malik, Aamir Saeed, Fofi, David, Saad, Naufal Mohamed, Ali, Yasir S., Meriaudeau, Fabrice
• Format: Article
• Language: English
• Subjects: Ballistocardiography; Biomedical measurement; Blood; Color; Correlation coefficients; Ejection; Estimation; Face; Force; Harmonic oscillation; Head; Head movement; Health; Heart; Heart beat; Human-computer interface; Illumination; Instrumentation and Detectors; Mean square values; Measurement methods; Methods; non-contact; photoplethysmography and ballistocardiography; Physics; Remote health monitoring; Remote monitoring; Robustness; Variance; video analytics
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2017.2708133

Video-based heartbeat rate measurement is a rapidly growing application in remote health monitoring. Video-based heartbeat rate measuring methods operate mainly by estimating photoplethysmography or ballistocardiography signals. These methods operate by estimating the microscopic color change in the face or by estimating the microscopic rigid motion of the head/ facial skin. However, the robustness to motion artifacts caused by illumination variance and motion variance of the subject poses main challenge. We present a video-based heartbeat rate measuring framework to overcome these problems by using the principle of ballistocardiography. In this paper, we proposed a ballistocardiography model based on Newtons third law of force and dynamics of harmonic oscillation. We formulate a framework based on the ballistocardiography model to measure the rigid involuntary head motion caused by the ejection of the blood from the heart. Our proposed framework operates by estimating the motion of multivariate feature points to estimate the heartbeat rate autonomously. We evaluated our proposed framework along with existing video-based heartbeat rate measuring methods with three databases, namely; MAHNOB HCI database, human-computer interaction database, and driver health monitoring database. Our proposed framework outperformed existing methods by reporting a low mean error rate of 4.34 bpm with a standard deviation of 3.14 bpm, root mean square error of 5.29 with a high Pearson correlation coefficient of 0.91. The proposed method also operated robustly in the human-computer interaction database and driver health monitoring database by overcoming the issues related to illumination and motion variance.