PERFECT: Personalized Exercise Recommendation Framework and architECTure

被引：0

作者：

Mehrabadi, Milad Asgari ^{[1
]}

Khatibi, Elahe ^{[1
]}

Jimah, Tamara ^{[2
]}

Labbaf, Sina ^{[1
]}

Borg, Holly ^{[3
]}

Narvaez, Laura ^{[3
]}

Pimentel, Pamela ^{[3
]}

Turner, Arlene ^{[4
]}

Dutt, Nikil ^{[1
]}

Guo, Yuqing ^{[3
]}

Rahmani, Amir M. ^{[1
]}

机构：

[1] Department of Computer Science, University of California, Irvine, Irvine,CA, United States

[2] Bouvé College of Health Sciences, Northeastern University, Boston,MA, United States

[3] Sue & Bill Gross School of Nursing, University of California, Irvine, Irvine,CA, United States

[4] First 5 Orange County Children & Families Commission, Santa Ana,CA, United States

来源：

ACM Transactions on Computing for Healthcare | 2024年 / 5卷 / 04期

基金：

芬兰科学院;

关键词：

Active Learning - Additional key word and phrasesreinforcement learning - Contextual banditti - Health benefits - Intervention - Key words - Mobile health systems - Personalizations - Physical activity - Reinforcement learnings;

D O I：

10.1145/3696425

中图分类号：

学科分类号：

摘要：

Background: The health benefits of regular physical activity (PA) are well-established and widely acknowledged. Through the integration of wearable trackers, the Internet of Things (IoT) - a network of interconnected devices capable of collecting and exchanging data - coupled with mobile health (mHealth), which refers to the use of mobile devices to support medical and public health practices, it is now feasible to systematically gather and present individual exercise behaviors. This advanced approach enables the precise correlation of users' physiological data and daily activities with their specific fitness needs, offering a personalized pathway to improving health outcomes.Objective: This study aims to enhance PA levels among individuals by developing a personalized exercise recommendation system. Utilizing reinforcement learning, the system proposes tailored exercise plans based on biomarkers and the user's specific context.Methods: In this study, we developed applications for smartphones and smartwatches designed to gather, monitor, and recommend exercise routines through the application of a contextual multi-arm bandit algorithm. To evaluate the efficacy of this mHealth exercise regimen, we enlisted the participation of twenty female college students.Results: The outcomes of our investigation revealed a significant enhancement in the average daily duration of exercise (P . 001). Participants expressed high levels of satisfaction with both the walking program and the recommendation system, achieving average ratings of 4.31 (SD 0.60) and 3.69 (SD 0.95), respectively, on a 5-point scale. Furthermore, the average scores for participants' confidence in safely performing the recommended walking exercises, as well as their perception of the study's effectiveness in meeting their PA needs, were both above 4, indicating a positive reception and confidence in the program's design and implementation.Conclusions: The evolution of the IoT and wearable technology has marked the beginning of a new era for mHealth systems, particularly in the personalization of health interventions. Such advancements enable the precise personalization of PA recommendations, potentially enhancing user engagement and performance outcomes. This paper introduces a novel exercise recommendation system that utilizes reinforcement learning to personalize walking exercises based on the user's biomarkers and context, aiming to improve the user's aerobic capacity significantly. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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