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Seismocardiography (SCG) has attracted significant interest for monitoring cardiac health and diagnosing cardiovascular conditions. While traditional SCG methods rely on uncomfortable chest-mounted accelerometers, recent research explores non-contact approaches, including analyzing video recordings of the chest. In this study, three computer vision-based methods including Lucas-Kanade optical flow, template tracking, and Gunnar-Farneback optical flow were evaluated for extracting SCG signals from ordinary camera-recorded chest videos. The study focused on right-to-left and head-to-foot SCG signals obtained from 13 healthy subjects during breath-hold at the end of exhalation and inhalation. Comparative analysis was performed by calculating the mean squared error (MSE) and root MSE (RMSE) between the vision-based SCG signals and the gold-standard accelerometer signals. Visual and quantitative analyses showed that the Lucas-Kanade and template tracking methods estimated vision-based SCG signals closely resembling the accelerometer data, particularly in the head-to-foot direction. The Lucas-Kanade method had MSE values ranging from 0.14 to 0.93, RMSE values from 0.38 to 0.96, average correlation values of 0.82±0.09. The template tracking method showed MSE values between 0.12 to 0.94, RMSE values from 0.35 to 0.97, and average correlation values of 0.83±0.10. In comparison, the Farneback method had higher MSE values ranging from 0.20 to 1.07, RMSE values from 0.44 to 1.03, and average correlation values of 0.76±0.11. These results suggest the effectiveness of Lucas-Kanade and template tracking methods for non-contact SCG signal extraction from chest video data. 

Introduction:Seismocardiography (SCG) - measurements of cardiovascular-induced vibrations on the chest - has shown potential for providing clinical information for cardiac conditions. SCG is conventionally recorded by an accelerometer attached to a single point on chest. Recent research suggests multichannel SCG (mSCG) - measurements from multiple chest locations - can provide extra and more accurate clinical information. Current mSCG methods are limited to accelerometer arrays, laser Doppler vibrometry, and airborne ultrasound that are either costly, difficult for inexperienced users, or need bulky equipment, thereby impeding their use beyond research or clinical settings. Hypothesis:mSCG signals can be accurately estimated from tiny chest movements in chest videos recorded by ordinary cameras, e.g., those in smartphones. Methods:We enrolled 10 subjects (sbjs) with no history of CVDs (21.7 ± 1.7 years, 40% women). ECG and chest video of sbjs were recorded at rest for 15 sec during breath hold at the end of inhalation followed by another 15 sec recording during breath hold at the end of exhalation. We developed an AI-powered mobile app to record the chest videos and convert them to 0-30 Hz mSCG in right-to-left (RL) and head-to-foot (HF) directions (Fig 1a). Heart rate (HR) based on ECG RR interval and mSCG was measured and compared. Results:HR estimated from mSCG in both RL and HF directions had a good agreement with ECG-based HR using Bland-Altman analysis [RL: bias = 1.4 bpm, 95% CI = 5.6 bpm; HF: bias = 0.8 bpm, 95% CI = 6.2 bpm (Fig 1b)]. High-quality mSCG and ECG measurements were obtained for all sbjs. Conclusion:Clinically relevant information can be accurately extracted from chest videos using our novel, contactless, AI-based method. Given that the vast majority of Americans have access to a camera phone, future developments of this method may provide new means of remote and accessible cardiac monitoring.