  Heart Rate Variability (HRV) measures the variation of the time between
consecutive heartbeats and is a major indicator of physical and mental health.
Recent research has demonstrated that photoplethysmography (PPG) sensors can be
used to infer HRV. However, many prior studies had high errors because they
only employed signal processing or machine learning (ML), or because they
indirectly inferred HRV, or because there lacks large training datasets. Many
prior studies may also require large ML models. The low accuracy and large
model sizes limit their applications to small embedded devices and potential
future use in healthcare. To address the above issues, we first collected a
large dataset of PPG signals and HRV ground truth. With this dataset, we
developed HRV models that combine signal processing and ML to directly infer
HRV. Evaluation results show that our method had errors between 3.5% to 25.7%
and outperformed signal-processing-only and ML-only methods. We also explored
different ML models, which showed that Decision Trees and Multi-level
Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds
of KB and inference time less than 1ms. Hence, they are more suitable for small
embedded devices and potentially enable the future use of PPG-based HRV
monitoring in healthcare.
