首页|Data on Machine Learning Described by Researchers at Department of Data Science and Artificial Intelligence (Enhancing digital health services: A machine learning approach to personalized exercise goal setting)
Data on Machine Learning Described by Researchers at Department of Data Science and Artificial Intelligence (Enhancing digital health services: A machine learning approach to personalized exercise goal setting)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Sage
New research on artificial intelligence is the subject of a new report. According to news reporting from Melbourne, Australia, by NewsRx journalists, research stated, "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." Financial supporters for this research include National Natural Science Foundation of China. The news reporters obtained a quote from the research from Department of Data Science and Artificial Intelligence: "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 analyze time series data and infer user's 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. The 95% confidence intervals demonstrated the robustness of these findings, emphasizing the superior outcomes of the machine learning approach."
Department of Data Science and Artificial IntelligenceMelbourneAustraliaAustralia and New ZealandAlgorithmsCyborgsEmerging TechnologiesMachine LearningReinforcement Learning
NETL
NSTL
Sage
