Abstract
Malnutrition is caused by the disparity between the amount of food nutrients that a body needs and the amount that the body receives via food. People who are living in remote areas or in rural areas are observed to suffer from malnutrition. Malnutrition is the case of the people living in rural areas and is a major concern for the overall health of the people. Malnutrition develops very slowly over the years. Changes at the cellular level begin to happen after a prolonged period of malnutrition. As a result of the malnutrition, the body loses its ability to fight against infections. For a prolonged period of time, many symptoms of the disease start emerging like anemia, edema, and goiter, so proper diagnosis and information collection are very necessary to find a remedy for this health problem. Internet of things (IoT) can play a great role in conjunction with an embedded system that will be used to collect data from those remote areas and be used to make a detailed analysis of the diseases caused by malnutrition. In this chapter, the detection and monitoring of nutrition levels with the help of proper embedded systems and IoT will be discussed. The design of proper embedded systems and the required interfacing programs for this purpose is a major issue. The proposed embedded system should work with Internet-connected devices [smartphone, other embedded devices] to be chosen very carefully with properties like low power consummation, low cost, and with the advanced interface for IoT. The embedded system should also be capable of exchanging data and commands via GPRS in the case where GSM networks are available or via Bluetooth transmission. The embedded system to be discussed also is having an interface with an android app for data exchange/control via smartphone.
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Dey, B. (2021). Design of an Embedded System for Remote Monitoring of Malnutrition for People Living in Rural Areas. In: Chakraborty, C., Banerjee, A., Kolekar, M., Garg, L., Chakraborty, B. (eds) Internet of Things for Healthcare Technologies. Studies in Big Data, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-15-4112-4_11
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DOI: https://doi.org/10.1007/978-981-15-4112-4_11
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