Multitask deep learning models for PPG analysis significantly outperformed baseline single-task models, achieving higher accuracy in quality assessment and reduced error rates in HR and RR estimation.
Does a multitask learning approach improve the accuracy of PPG quality assessment and physiological parameter estimation compared to single-task models?
Multitask deep learning models improve the accuracy and reliability of physiological parameter estimation from wearable PPG sensors compared to traditional single-task approaches.
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.
Feli et al. (Wed,) conducted a other in Free-living conditions (n=46). Multitask deep learning models vs. Baseline single-task models was evaluated on PPG quality assessment accuracy and HR/RR estimation error rates. Multitask deep learning models for PPG analysis significantly outperformed baseline single-task models, achieving higher accuracy in quality assessment and reduced error rates in HR and RR estimation.