Bed based ballistocardiography (BCG) is a prime candidate for at home and nighttime monitoring especially in the growing elderly population because co-operation from the user is not required to be able to record signals. One issue with BCG is that the signal quality has intraand inter-person variability based on factors such as age, gender, body position, and motion artifacts, making it challenging to accurately measure heart rate.

A rule-based algorithm which considers all eight available BCG channels simultaneously from a given time epoch was developed using continuous wavelet transform (CWT) to extract the localized time-frequency representation of each epoch and then an averaging method was applied across the different scales of the CWT to produce a 1-dimensional array. Autocorrelation was then applied to this array to produce a heart rate estimate based on the lag between the autocorrelation maximum and the first side peak. This method does not require identification of individual heart beats to estimate heart rate and does not require annotated training data.

This model produces an average mean absolute error (MAE) of 1.09 bpm across 40 subjects when compared to heart rate derived from ECG. This method produces competitive results without the need for annotated training data, which can be challenging to collect.