ArticleSensors (Basel, Switzerland)2025
A Personalized Energy Expenditure Estimation Method Using Modified MET and Heart Rate-Based DQN.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Comparative Validity of Smartwatch-Derived Heart Rate and Energy Expenditure During Endurance and Resistance Exercise.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
2 authors.
Funding
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.
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Registered trials
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