Hybrid obesity monitoring model using sensors and community engagement
RIS ID
116138
Abstract
Obesity has been recognized to be among the principal causes of many chronic diseases such as diabetes, cholesterol, hypertension, and other cardiovascular diseases. Therefore, monitoring, controlling, and preventing obesity will mitigate the risks generated from the complications of these diseases. Comprehensive preventive measures are essential to control the spread of obesity, while healthcare systems should be organized on the basis of locally derived data to provide adequate and affordable care to the increasing groups of overweight and obese people. In this paper, we propose a hybrid model that relies on both data collected from sensors and participatory data collected from a social network community established to provide value-added obesity awareness, monitoring, and prevention. The model encompasses some key smart features including tracking food intake, lifestyle, and exercise activities, generating warnings and recommendations, and triggering interventions whenever needed. Our model also mines the collected data to produce statistical analysis that can be used by health authorities to have a clear picture of the health status of the population and might help in making rational and informed decisions. Moreover, we implement a prototype of our model as a set of Web services using the SOA paradigm and lightweight protocols. Promising results of our prototype are reported and analyzed.
Publication Details
Harous, S., Serhani, M. Adel., El Menshawy, M. & Benharref, A. 2017, 'Hybrid obesity monitoring model using sensors and community engagement', 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017, IEEE, United States, pp. 888-893.