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Abstract Cardiovascular diseases remain the leading cause of mortality worldwide, underscoring the need for improved diagnostic tools. Seismocardiography (SCG), a noninvasive technique that records chest surface vibrations generated by cardiac activity, holds promise for such applications. However, the mechanistic origins of SCG waveforms, particularly under varying physiological conditions, remain insufficiently understood. This study presents a finite element modeling approach to simulate SCG signals by tracking the propagation of cardiac wall motion to the chest surface. The computational model, constructed from 4D CT scans of healthy adult subjects, incorporates the lungs, ribcage, muscles, and adipose tissue. Cardiac displacement boundary conditions were extracted using the Lucas-Kanade algorithm, and elastic properties were assigned to different tissues. The simulated SCG signals in the dorsoventral direction were compared to realistic SCG recordings, showing consistency in waveform morphology. Key cardiac events, such as mitral valve closure, aortic valve opening, and closure, were identified on the modeled SCG waveforms and validated with concurrent CT images and left ventricular volume changes. A systematic sensitivity analysis was also conducted to examine how variations in tissue properties, soft tissue thickness, and boundary conditions influence SCG signal characteristics. The results highlight the critical role of personalized anatomical modeling in accurately capturing SCG features, thereby improving the potential of SCG for individualized cardiovascular monitoring and diagnosis. 

Abstract Seismocardiography (SCG) is a technique that non-invasively measures the chest wall’s local vibrations caused by the heart’s mechanical activity. Traditionally, SCG signals have been recorded using accelerometers placed at a single location on the chest wall. This study presents an innovative, cost-effective SCG method that utilizes standard smartphone videos to capture data from multiple chest locations. The analysis of vibrations from multiple points can offer a more thorough understanding of the heart’s mechanical activity compared to signals obtained solely from a single chest location. Our approach employs computer vision and deep learning techniques to extract and improve the resolution of multichannel SCG maps obtained by video capture of chest movement. We attached a grid of patterned stickers to the chest surface and recorded videos of chest movements during different respiratory phases. Using a deep learning-based object detector and a template tracking method, we tracked the stickers across video frames and extracted the corresponding SCG signals from sticker displacements. We also developed a robust algorithm to estimate heart rate (HR) from these chest videos and identify the optimal chest location for HR estimation. The method was tested on 28 chest videos captured from 14 healthy participants. The results demonstrated that our method effectively extracted multichannel SCG maps and enhanced their resolution with a mean squared error of 0.1078 and 0.0418 for right-to-left and head-to-foot SCG signals, respectively. We observed intersubject chest vibration patterns corresponding to cardiac events including opening and closure of the heart valves. Moreover, our algorithm accurately estimated HR from 1968 SCG signals extracted from the videos compared to the gold-standard HR measured from each subject’s electrocardiogram (bias ± 1.96 SD = 0.04 ± 2.14 bpm;r = 0.99,p < 0.001). The findings from this study underscore the potential of our approach in developing a cardiac monitoring tool using a smartphone that would be widely accessible to the general public and might provide more timely detection of diseases.