What question did this study set out to answer?

To develop a neural network model that accurately predicts the relationship between metabolic rate and heart rate during physical activities.

January 10, 2026Computer Methods and Programs in Biomedicine0 citations

Physiological-model-based neural network for modeling the metabolic-heart rate relationship during physical activities

YZYaowen ZhangBeijing Institute of Technology LFLibera FresielloHeart Failure & Transplant PVP. H. VeltinkVrije Universiteit Amsterdam

Key Points

To develop a neural network model that accurately predicts the relationship between metabolic rate and heart rate during physical activities.
Developed a physiological model integrating metabolic rate and heart rate data.

Structured PICO

Population

25 participants (aged 12 to 40, median 18) from two public datasets (WEEE and ACTES) engaged in physical activities including resting, cycling, and running.

Intervention

Physiological-model-based neural network (PMB-NN) framework to model the oxygen uptake (VO2)-heart rate (HR) relationship

Comparator

Fully connected neural network (FCNN) and a simplified physiological model (PM)

Outcome

Heart rate estimation performance evaluated across numerical accuracy, physiological plausibility, and physiological interpretability

The PMB-NN framework accurately estimates individualized heart rate from oxygen uptake during physical activities, providing a foundation for personalized digital twins in cardiac monitoring.

Abstract

This study confirms the validity of the PMB-NN framework using precise metabolic inputs. This foundational validation enables future integration with wearable-based V̇O2 estimation systems, ultimately paving the way for personalized, real-time cardiac monitoring during daily life physical activities to enhance HF risk detection.

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