While recent advances have shown that it is possible to acquire a signal equivalent to the heartbeat from
visual spectrum video recordings of the human skin, extracting the heartbeat’s exact timing information from it, for
the purpose of heart rate variability analysis, remains a challenge. In this paper, we explore two novel methods to
estimate the remote cardiac signal peak positions, aiming at a close representation of the R-peaks of the ECG signal.
The first method is based on curve fitting (CF) using a modified filtered least mean square (LMS) optimization and
the second method is based on system estimation using blind deconvolution (BDC). To prove the efficacy of the
developed algorithms, we compared results obtained with the ground truth (ECG) signal. Both methods achieved a
low relative error between the peaks of the two signals. This work, performed under an IRB approved protocol,
provides initial proof that blind deconvolution techniques can be used to estimate timing information of the cardiac
signal closely correlated to the one obtained by traditional ECG. The results show promise for further development of
a remote sensing of cardiac signals for the purpose of remote vital sign and stress detection for medical, security,
military and civilian applications.