Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation

Abstract

Wearable technology has expanded the applications of photoplethysmography (PPG) in remote health monitoring, enabling real-time measurement of various physiological parameters, such as heart rate (HR), heart rate variability (HRV), and respiration rate (RR). While existing studies mainly focus on individual parameters derived from PPG, they often overlook the shared characteristics among these physiological parameters. Multitask learning (MTL) offers a promising solution by training a single model to perform multiple related tasks, leveraging their interdependencies. However, the potential of MTL has not been thoroughly investigated in the context of PPG analysis. In this paper, we develop MTL approaches that exploit shared underlying characteristics across PPG-related tasks to improve the performance of PPG-based applications. We propose customized multitask deep learning models for two applications: (1) PPG quality assessment for HR and HRV features collected in free-living conditions and (2) simultaneous HR and RR estimation from PPG. Our models are evaluated on a PPG dataset collected from 46 subjects wearing smartwatches during their daily activities. Results demonstrate that the proposed MTL methods significantly outperform baseline single-task models, achieving higher accuracy in quality assessment and reduced error rates in HR and RR estimation.