Abstract
Poor nutrition impairs the health and wellbeing of the population and increases the risk of chronic diseases such as obesity and type 2 diabetes. Chronic diseases that require dietary management can be better managed if food and nutrition intake is monitored. Existing methods for measurement are inaccurate and not scalable as they are based on a person’s ability to recall and self-report. In this paper, we propose a smart nutrition monitoring system based on Internet of Things (IoT) technologies to collect reliable nutrition intake data from heterogeneous sensors. The proposed method is non-invasive and consists of a combination of data sources from heterogeneous devices to increase accuracy. The system architecture is based on emerging Fog Computing concepts where data collection points are able to do the preprocessing and lightweight analytics before sending data to the Cloud. The system prototype is developed using various sensors including cameras to generate 3D images for food volume estimation.
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Notes
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See, for example, https://www.fatsecret.com, which maintains such a database that can be accessed via RESTful APIs.
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Javadi, B., Calheiros, R.N., Matawie, K.M., Ginige, A., Cook, A. (2018). Smart Nutrition Monitoring System Using Heterogeneous Internet of Things Platform. In: Fortino, G., Ali, A., Pathan, M., Guerrieri, A., Di Fatta, G. (eds) Internet and Distributed Computing Systems. IDCS 2017. Lecture Notes in Computer Science(), vol 10794. Springer, Cham. https://doi.org/10.1007/978-3-319-97795-9_6
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