Enhancing Digital Health Services: A Machine Learning Approach to
Personalized Exercise Goal Setting
- URL: http://arxiv.org/abs/2204.00961v3
- Date: Mon, 4 Mar 2024 05:38:44 GMT
- Title: Enhancing Digital Health Services: A Machine Learning Approach to
Personalized Exercise Goal Setting
- Authors: Ji Fang, Vincent CS Lee, Hao Ji, Haiyan Wang
- Abstract summary: This study aims to develop a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory.
The deep reinforcement learning algorithm combines deep learning techniques to analyse time series data and infer user exercise behavior.
- Score: 8.146832452474777
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The utilization of digital health has increased recently, and these services
provide extensive guidance to encourage users to exercise frequently by setting
daily exercise goals to promote a healthy lifestyle. These comprehensive guides
evolved from the consideration of various personalized behavioral factors.
Nevertheless, existing approaches frequently neglect the users dynamic behavior
and the changing in their health conditions. This study aims to fill this gap
by developing a machine learning algorithm that dynamically updates
auto-suggestion exercise goals using retrospective data and realistic behavior
trajectory. We conducted a methodological study by designing a deep
reinforcement learning algorithm to evaluate exercise performance, considering
fitness-fatigue effects. The deep reinforcement learning algorithm combines
deep learning techniques to analyse time series data and infer user exercise
behavior. In addition, we use the asynchronous advantage actor-critic algorithm
for reinforcement learning to determine the optimal exercise intensity through
exploration and exploitation. The personalized exercise data and biometric data
used in this study were collected from publicly available datasets,
encompassing walking, sports logs, and running. In our study, we conducted The
statistical analyses/inferential tests to compare the effectiveness of machine
learning approach in exercise goal setting across different exercise goal
setting strategies.
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