  Purpose. Photoplethysmography (PPG) is a non-invasive technique that measures
changes in blood flow volume through optical means. Previous research has
established the feasibility of PPG peak detection based on the crossover of
moving averages. This paper proposes the Smoothed Recuarsive Moving Average
Crossover, which eliminates the need for post-processing and nonlinear
pre-processing of previous crossover-based peak detectors. The proposed model
is advantageous regarding memory and computational complexity, making it
attractive for implementations on embedded devices.
  Methods. Along with this paper, we make available a novel dataset comprising
66 minutes of PPG recordings. The optimization and assessment of the proposed
peak detection model use this dataset. Its optimization is accomplished with
the simple random search heuristic, while the leave-subject-out
cross-validation method provides the means to assess its performance. The
source code for all experiments reported in this research is also available in
an online repository.
  Results. The experimental study examines the performance of the proposed
model considering different arrangements of the PPG data. The experiments show
that the proposed model performs better than the previous crossover-based
approach from the literature regarding the precision and recall metrics. More
specifically, our model has an average precision of 0.9937 and an average
recall of 0.9968.
  Conclusion. The contribution of this research to the scientific community and
literature is twofold. The dataset we collected is open for any researcher, and
we improve upon the leading edge on crossover-based PPG peak detection. This
improvement comes in terms of performance metrics and computational cost.
