Home Based Healthcare Monitoring System for Diabetes Patients Using IoT

  • C. S. Krishna
  • T. SasikalaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


Healthcare Monitoring system introduces to decrease the daily visit of patients to the hospital. A patient with chronic disease such as diabetic which includes type-1 and type-2 diabetic, has to be constantly monitor his glucose level to minimize the risk. According to human physiology, insulin and glucagon are hormones. Those hormones help body to balance blood glucose level. As per “Journal of Clinical Investigation”, the blood glucose level grow with the secretion of glucagon. The blood glucose level decreases as per the release of insulin. The duration of sleep the body get affects body’s release of hormones (glucagon and insulin). Less sleep has been shown to rise blood glucose levels and the risk of diabetic issues. Higher the blood glucose represents less long-lasting fat metabolism in the night and even less sleep. The monitoring system uses a wearable device on wrist, which includes accelerometer, gyroscope, SpO2 and heart rate sensor, can be analyse the sleep pattern and the motion of the patient can be analyse condition of the patient with all the parameters which includes oxygen saturation percentage, heart rate, and blood glucose. The patient can collect their vital parameters and send it to the doctor or hospital. Healthcare monitoring system provides the real-time monitoring of vital parameters of the patients. Collecting all the information from the patient’s and send it to the webserver. The authorized person can login to his account and see the patient’s vital parameters. These information can be stored and analysed for further analysis and decision making. By determining the pattern of the parameters which is observed, the nature of the disease can be predicted. The system collects patient’s blood glucose using Raspberry Pi board and cloud computing. The collected data from various patients could not be handled easily by the physician. The main concern of the physician is that he should take the critical decisions about their patient’s health from these huge volume of health information. The vast amount of data can be stored in server and analyse the data using data mining. If the patient is alone at home and anything happened to him like he fell down or he is unconscious due to decrease in glucose level the data will be analysed and send a message to the ambulance and to the authorized person with the location.


Raspberry Pi Board Pulse oximeter Glucometer Internet of Things Cloud computing ThingSpeak Accelerometer Machine learning Knn-algorithm Logistic regression 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamBengaluruIndia

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