Physical activity prediction using fitness data: Challenges and issues

Nur Zarna Elya Zakariya,Marshima Mohd Rosli

doi:10.11591/eei.v10i1.2474

Nur Zarna Elya Zakariya, Marshima Mohd Rosli

Open Access

https://doi.org/10.11591/eei.v10i1.2474

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Abstract

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.

Highlights

Wearable technologies are devices and applications [1, 2] that popular nowadays for monitoring physical activities and prevention of diseases
We identified few parameters and features that most appropriate to be used in predicting the suitable physical activity based on personal context
We found two models that considered important parameters in their process for predicting physical activities using fitness data and using personal context data

Summary

Introduction

Wearable technologies are devices and applications [1, 2] that popular nowadays for monitoring physical activities and prevention of diseases. The devices and applications are designed to motivate individuals in monitoring their health, for example, diet tracking, weight control and physical activity tracking. People are encourage to tracking their fitness data and maintain healthy diet to stay healthy and avoid risks of critical disease [3]. Some wearable devices and applications tracking fitness data but the applications able to recommend a general physical activity to stay healthy. Several initiatives have been explored in recent years to encourage physical activity with wearable technologies, smartphones [5,6,7].

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