  Respiratory rate (RR) can be estimated from the photoplethysmogram (PPG)
recorded by optical sensors in wearable devices. The fusion of estimates from
different PPG features has lead to an increase in accuracy, but also reduced
the numbers of available final estimates due to discarding of unreliable data.
We propose a novel, tunable fusion algorithm using covariance intersection to
estimate the RR from PPG (CIF). The algorithm is adaptive to the number of
available feature estimates and takes each estimates' trustworthiness into
account. In a benchmarking experiment using the CapnoBase dataset with
reference RR from capnography, we compared the CIF against the state-of-the-art
Smart Fusion (SF) algorithm. The median root mean square error was 1.4
breaths/min for the CIF and 1.8 breaths/min for the SF. The CIF significantly
increased the retention rate distribution of all recordings from 0.46 to 0.90
(p $<$ 0.001). The agreement with the reference RR was high with a Pearson's
correlation coefficient of 0.94, a bias of 0.3 breaths/min, and limits of
agreement of -4.6 and 5.2 breaths/min. In addition, the algorithm was
computationally efficient. Therefore, CIF could contribute to a more robust RR
estimation from wearable PPG recordings.
