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Determining the Contribution of the Energy Systems During Exercise

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A Personalized Energy Expenditure Estimation Method Using Modified MET and Heart Rate-Based DQN.

Min-Seo Kim¹, Ju-Hyeon Seong¹

¹Department of Maritime AI and Cyber Security & Interdisciplinary Major of Maritime AI Convergence, National Republic of Korea Maritime & Ocean University, 727, Taejong-ro, Yeongdo-gu, Busan 49112, Republic of Korea.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces a new method for real-time energy expenditure estimation using wearable devices. The novel approach enhances accuracy by integrating heart rate data with a Deep Q-Network for personalized activity intensity.

Keywords:

MET energy expenditure estimation (EEE)reinforcement learning (RL)wearable device

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Area of Science:

Biomedical Engineering
Wearable Technology
Personalized Health

Background:

Wearable devices integrate bio-signals for personalized services.
Accurate real-time energy expenditure estimation is challenging due to movement and measurement limitations.

Purpose of the Study:

To propose a novel real-time and personalized energy expenditure estimation (EEE) method.
To address the limitations in accurately and rapidly estimating energy expenditure (EE) from wearable devices.

Main Methods:

Developed Real-Time Energy Expenditure (RTEE), a novel method for EEE.
Integrated a Deep Q-Network (DQN)-based activity intensity coefficient inference network.
Utilized a modified energy consumption prediction algorithm with real-time heart rate variations.

Main Results:

The RTEE method enables real-time and personalized energy expenditure estimation.
The approach leverages DQN for activity intensity and heart rate data for prediction.
The algorithm demonstrates applicability to various heart rate-based EEE methods.

Conclusions:

The proposed RTEE method offers a significant advancement in wearable-based energy expenditure tracking.
This personalized approach enhances the accuracy and responsiveness of activity monitoring.
The integration of advanced AI with bio-signal data opens new possibilities for health and fitness applications.