Kim Min-Seo, Seong Ju-Hyeon
Department 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). 2025 May 29;25(11):3416. doi: 10.3390/s25113416.
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.
基于可穿戴设备的个人活动测量技术通过整合生物信号提供各种个性化服务。然而，由于用户运动和测量参数的限制，准确快速地估计能量消耗（EE）仍然具有挑战性。在本文中，我们提出了实时能量消耗（RTEE），一种新颖的实时个性化能量消耗估计（EEE）方法。所提出的RTEE将基于深度Q网络（DQN）的活动强度系数推理网络与改进的能量消耗预测算法相结合，以根据用户心率测量的实时变化来估计能量消耗。因此，所提出的算法可以应用于各种基于心率的能量消耗预测方法。
