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Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 2/2022

06-09-2021 | Research Article-Computer Engineering and Computer Science

Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique

Authors: Anshika Arora, Pinaki Chakraborty, M. P. S. Bhatia

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

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Abstract

Wearable devices equipped with sensors popularly used for health monitoring are capable of accumulating motion data providing objective measures of various physical activity and sleep attributes. Also, smartphone usage has grown to an extent where phones have become an integral part of lifestyle contributing to users’ screen viewing time. Behavior and behavioral attributes of individuals’ personal characteristics are significant components of lifestyle of which sleep, physical activity and screen viewing correspond to the most occupied time throughout the day and among the major lifestyle patterns affecting overall health. This study aims to assess sleep quality and behavioral health from wearables and smartphones using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Real time physical activity and sleep data have been collected from users’ smartwatches. A smartphone application is built to collect real time smartphone usage data. Sleep quality indicator (SleepQual) for assessment of daily sleep quality is calculated using sleep attributes collected from smartwatches. Correlation of SleepQual is evaluated with physical activity attributes and smartphone usage attributes using Pearson’s correlation. Highly correlated attributes along with sleep attributes are used to train the ANFIS model for sleep quality assessment. A novel behavioral health indicator (B. Health) is proposed which is evaluated using real time physical activity, sleep and screen time data. Attributes are ranked on the basis of Pearson’s correlation with B. Health to identify the most important contributors to behavioral health. Top ranked features are selected to train the ANFIS model for behavioral health assessment. Systematic Minority Oversampling Technique has been used for data augmentation. The ANFIS model achieves an accuracy of 91.69% for sleep quality assessment and 85.79% for behavioral health assessment.
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Metadata
Title
Intervention of Wearables and Smartphones in Real Time Monitoring of Sleep and Behavioral Health: An Assessment Using Adaptive Neuro-Fuzzy Technique
Authors
Anshika Arora
Pinaki Chakraborty
M. P. S. Bhatia
Publication date
06-09-2021
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-021-06078-5

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