Work

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

Year: 2025

Type: article

Abstract: Wearable device-based personal activity measurement technology provides various personalized services by integrating bio-signals. However, accurately and rapidly estimating energy expenditure (EE) remains challenging due to user movement and the limitations of measurement parameters. In this paper, we propose Real-Time Energy Expenditure (RTEE), a novel real-time and personalized energy expenditure estimation (EEE) method. The proposed RTEE integrates a Deep Q-Network (DQN)-based activity intensity coefficient inference network with a modified energy consumption prediction algorithm to estimate energy expenditure based on real-time variations in the user's heart rate measurements. Therefore, the proposed algorithm can be applied to various heart rate-based energy consumption prediction methods.

Source: Sensors

DOI: 10.3390/s25113416

Authors: Min-Seo Kim, Ju-Hyeon Seong

Institution: Korea Maritime and Ocean University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: Ministry of Trade, Industry and Energy, Korea Institute for Advancement of Technology, Ministry of SMEs and Startups