Physical activity prediction using fitness data: Challenges and issues

In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play...

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Bibliographic Details
Published in:Bulletin of Electrical Engineering and Informatics
Main Author: Zakariya N.Z.E.; Rosli M.M.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092363786&doi=10.11591%2feei.v10i1.2474&partnerID=40&md5=61c98214a8aefe569cd67b6df0a5f0e4
id 2-s2.0-85092363786
spelling 2-s2.0-85092363786
Zakariya N.Z.E.; Rosli M.M.
Physical activity prediction using fitness data: Challenges and issues
2021
Bulletin of Electrical Engineering and Informatics
10
1
10.11591/eei.v10i1.2474
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092363786&doi=10.11591%2feei.v10i1.2474&partnerID=40&md5=61c98214a8aefe569cd67b6df0a5f0e4
In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in Behavior Change Technique (BCT) and we selected two suitable models which are Fogg Behavior Model (FBM) and Trans-theoretical Behavior Model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health. © 2020, Institute of Advanced Engineering and Science. All rights reserved.
Institute of Advanced Engineering and Science
20893191
English
Article
All Open Access; Gold Open Access
author Zakariya N.Z.E.; Rosli M.M.
spellingShingle Zakariya N.Z.E.; Rosli M.M.
Physical activity prediction using fitness data: Challenges and issues
author_facet Zakariya N.Z.E.; Rosli M.M.
author_sort Zakariya N.Z.E.; Rosli M.M.
title Physical activity prediction using fitness data: Challenges and issues
title_short Physical activity prediction using fitness data: Challenges and issues
title_full Physical activity prediction using fitness data: Challenges and issues
title_fullStr Physical activity prediction using fitness data: Challenges and issues
title_full_unstemmed Physical activity prediction using fitness data: Challenges and issues
title_sort Physical activity prediction using fitness data: Challenges and issues
publishDate 2021
container_title Bulletin of Electrical Engineering and Informatics
container_volume 10
container_issue 1
doi_str_mv 10.11591/eei.v10i1.2474
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092363786&doi=10.11591%2feei.v10i1.2474&partnerID=40&md5=61c98214a8aefe569cd67b6df0a5f0e4
description In the new healthcare transformations, individuals are encourage to maintain healthy life based on their food diet and physical activity routine to avoid risk of serious disease. One of the recent healthcare technologies to support self health monitoring is wearable device that allow individual play active role on their own healthcare. However, there is still questions in terms of the accuracy of wearable data for recommending physical activity due to enormous fitness data generated by wearable devices. In this study, we conducted a literature review on machine learning techniques to predict suitable physical activities based on personal context and fitness data. We categorize and structure the research evidence that has been publish in the area of machine learning techniques for predicting physical activities using fitness data. We found 10 different models in Behavior Change Technique (BCT) and we selected two suitable models which are Fogg Behavior Model (FBM) and Trans-theoretical Behavior Model (TTM) for predicting physical activity using fitness data. We proposed a conceptual framework which consists of personal fitness data, combination of TTM and FBM to predict the suitable physical activity based on personal context. This study will provide new insights in software development of healthcare technologies to support personalization of individuals in managing their own health. © 2020, Institute of Advanced Engineering and Science. All rights reserved.
publisher Institute of Advanced Engineering and Science
issn 20893191
language English
format Article
accesstype All Open Access; Gold Open Access
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