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Continuous monitoring of inter-beat-interval (IBI) and heart rate variability(HRV) provides insights in cardiovascular, neurological, and mental health.Photoplethysmography (PPG) from wearables assures convenient measurement ofIBI. However, PPG is susceptible to motion artifacts, considerablydeteriorating the accuracy of IBIs estimation. Although a multi-channel modelin previous study improves accuracy, prevailing compact commercial wearableswould favor single-channel sensors, causing benefits of multi-channelapplications to have restrictions. In this paper, a greedy-optimized frameworkis proposed for measurement of IBI and HRV featuring single-channel andmulti-channel PPG signals collected during daily activities. Utilizing the factof continuity in heartbeats, the IBI estimation problem is converted into theshortest path problem in a directed acyclic graph, where candidate heartbeatsfrom the noisy PPG are regarded as vertices. The framework exploits a convexpenalty function to optimize weight assignment in the shortest path calculationand a greedy-optimized fusion method to mitigate overly fluctuating patterns inestimated IBIs. The results achieve correlation of 0.96 with percentage errorof 3.2% for IBI estimation using single-channel PPG signals from the 2015 IEEESignal Processing Cup dataset, where percentage error is reduced by 58.4% andcorrelation is improved by 11.6% in comparison to those withoutgreedy-optimized fusion. In the multi-channel model, it achieves correlation of0.98 with percentage error of 2.2%. Estimated and true HRV parameters are alsohighly correlated with low percentage errors. This paper further validatesthese techniques on the PPG-DaLiA dataset, indicating the robustness of theproposed framework.

A Greedy-optimized Framework for Heart Rate Variability Monitoring during Daily Activities using Wearable Photoplethysmography