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

## TL;DR

This paper introduces a new method for estimating energy expenditure in real-time using heart rate data and a modified DQN algorithm.

## Contribution

The novelty lies in combining a modified MET with a DQN-based approach for personalized energy expenditure estimation.

## Key findings

- The proposed RTEE method integrates a DQN-based network for activity intensity coefficient inference.
- The algorithm adapts to real-time heart rate variations for accurate energy expenditure estimation.
- The method is applicable to various heart rate-based energy prediction techniques.

## 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.