Research Article
Non-invasive Analytics Based Smart System for Diabetes Monitoring
@INPROCEEDINGS{10.1007/978-3-319-76213-5_13, author={M. Saravanan and R. Shubha}, title={Non-invasive Analytics Based Smart System for Diabetes Monitoring}, proceedings={Internet of Things (IoT) Technologies for HealthCare. 4th International Conference, HealthyIoT 2017, Angers, France, October 24-25, 2017, Proceedings}, proceedings_a={HEALTHYIOT}, year={2018}, month={2}, keywords={Diabetes monitoring Non-invasive method Sensors and devices PSO algorithm Mobile app}, doi={10.1007/978-3-319-76213-5_13} }- M. Saravanan
R. Shubha
Year: 2018
Non-invasive Analytics Based Smart System for Diabetes Monitoring
HEALTHYIOT
Springer
DOI: 10.1007/978-3-319-76213-5_13
Abstract
Wearable devices have made it possible for health providers to monitor a patient’s health remotely using actuators, sensors and other mobile communication devices. Internet of Things for Medical Devices is poised to revolutionize the functioning of the healthcare industry by providing an environment where the patient data is transmitted via a gateway onto a secure cloud based platforms for storage, aggregation and analytics. This paper proposes new set of wearable devices - a smart neck band, smart wrist band and a pair of smart socks - to continuously monitor the condition of diabetic patients. These devices consist of different sensors working in tandem form a network that reports food intake, heart rate, skin moisture, ambient temperature, walking patterns and weight gain/loss. The devices with the aid of controllers send all the sensor values as a packet via Bluetooth to the Mobile App. With the help of Machine Learning algorithm, we have predicted the change in patient status and alert them.
