Abstract
A novel approach to extract a heart rate signal from video footage consisting of a five stage processing pipeline is presented. Two extraction methods were used to obtain a heart rate. The first used the Fast Fourier transform to estimate an average heart rate by peak frequency analysis in the frequency distribution and estimated heart rates with a MAE as small as 2.32 BPM. This MAE value is smaller than those found by previous research which used PPG signals and BCG signals to extract a heart rate. The second approach used the Short-time Fourier transform to produce a time series of heart rate estimation which, when compared to accepted ground truths produced a covariance value of up to 0.9206335. Using a hybrid CNN-LSTM model an ECG-like signal was extracted from time-series heart beat waveforms. The resultant ECG-like signal displayed some of the characteristic ECG traits however it was not stable across the entire time period. Potentially, such a non-invasive heart monitoring can serve as a remote healthcare surveillance tool for smart homecare.
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Harrison, T., Zhang, Z., Jiang, R. (2022). Video-Based Heart Rate Detection: A Remote Healthcare Surveillance Tool for Smart Homecare. In: Jiang, R., et al. Big Data Privacy and Security in Smart Cities. Advanced Sciences and Technologies for Security Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-04424-3_10
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