Mobile health architecture for obesity management using sensory and social data
Open accessAbstract
One of the principal causes of several chronic diseases (e.g., diabetes, high cholesterol, and hypertension) is the obesity epidemic in high and middle income countries. Obesity also leads to an increasingly negative effect on public health resources. Therefore, obesity and overweight have to be monitored to mitigate and prevent the potential risks generated from the threat of related diseases and from reducing productivity experienced by businesses. A mobile-health monitoring system includes sensing, transmitting, storing, processing, and analyzing intensive, continuous, and heterogeneous medical data. However, current approaches are standalone mobile applications, augmented mobile applications, or mobile health systems. These approaches only consider simple activities (assess, detect, or control obesity) and rely on a mobile phone to perform complex processing operations on the collected data. Such complex operations need (1) efficient data mining techniques, (2) more memory consumption and processing time, and (3) long life mobile battery. In this work, we develop a new comprehensive mobile architecture for tackling the challenging issues of obesity control, monitoring, and prevention. We introduce a set of business requirements considering stakeholders, sensor devices, and architecture requirements to meet our architecture's objectives. Our architecture system can also help individuals track food intake, lifestyle, calories intake, calories consumption, and exercise activities. We analyze the data collected from continuous monitoring using non-invasive sensors, in addition to the data collected from social communities created to propagate awareness and share appropriate information about the obesity problem and its solution. We develop data mining algorithms and sentiment analysis algorithms and generate intelligent suggestions, warnings, and recommendations to control and mitigate the risk of obesity and its related diseases. We develop schemes for reducing data and saving energy, which minimize the amount of network traffic within the community of sensors. Moreover, we totally implement our architecture system as a collection of Web services organized by the model–view–controller design pattern to write, retrieve, and access data to and from the cloud storage firebase. We finally evaluate the efficacy and scalability of the implemented system using a comprehensive cloud database including entered data, calculated data, sensory data, and social data of 50 underweight, overweight, normal, and obese volunteer subjects. The obtained results show our architecture's objectives are fulfilled.
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Using the newly developed mobile architecture, one can tackle the challenges of obesity control, monitoring, and prevention.
“Using this newly developed mobile architecture, one can tackle the challenges of obesity control, monitoring, and prevention.”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [40]
Harous et al. enlightened applications of big data for obesity management using sensory and social data in mobile health architecture, mainly proposed for assessing, detecting, and controlling obesity, depend on a mobile phone to be used for complex operations and processing of the data collected.
“Harous et al. [40] have enlightened the applications of big data for obesity management using sensory and social data in mobile health architecture, which are mainly proposed for assessing, detecting, and controlling obesity, which depend on a mobile phone to be used for complex operations and processing of the data collected.”
From Influential Usage of Big Data and Artificial Intelligence in Healthcare (2021) · cites this paper as [40]
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- Authors
- Harous, S., El Menshawy, M., Serhani, M. A., & Benharref, A.
- Affiliations
- United Arab Emirates University (AE)
- University of Wollongong in Dubai (AE)
- Corresponding author
- Serhani, Mohamed Adel
- Venue
- Informatics in Medicine Unlocked
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 10
- Pages
- 27-44
- ISSN
- 2352-9148 (ISSN-L)
- OpenAlex
- W2773052278
- Cited by
- 28
- References
- 40
- Citations by year
- 2024: 1 · 2023: 3 · 2022: 4 · 2021: 4 · 2020: 7 · 2019: 7 · 2018: 2
- FWCI
- 7.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Informatics in Medicine Unlocked (publishedVersion, cc-by-nc-nd)
- Research Online (University of Wollongong) (submittedVersion)
- Funders
- UAEU/UPAR (31T066)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- mobile health, obesity treatment, obesity monitoring, non-invasive sensors, wearable sensors, dietary monitoring, calorie tracking, exercise monitoring, sentiment analysis, health recommendation system, energy-efficient data transmission, cloud-based health data, Firebase, Model-View-Controller
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