A Smart and Secured Approach for Children’s Health Monitoring Using Machine Learning Techniques Enhancing Data Privacy

Abstract

Health monitoring of the infant and child is proposed to provide the assurance to the parents of the infant and child. Several existing features are initiated where the proximity is not identified. Everyday varied health conditions of the infant (0–4 years) and child (6–10 years) are attained to monitor their health constantly. Different vital parameters such as heart rate, blood pressure, temperature and emotions are detected using the sensors. Smart devices such as IR photodetector and wearable gadget sensors are proposed to monitor the health condition of the child and infant. To attain the movement of the infant and child, such as intrusion detection, the accelerometer is used and to monitor the kid, the camera is used to detect the intrusion. To monitor the child’s safety outside the environment, the GPS trackers with GSM are fixed to predict the necessary safety condition. IOT monitoring framework detects the zone and captures the information and sends notification to the parents. Different Machine learning algorithms are used to obtain the suitable output with accuracy and performance measures. The IOT monitoring and evaluated data are cross-validated to attain the precise results. The comparative result analysis of all the different algorithms is analysed. In this proposed architecture, the Blockchain technology is used to provide data security and to avoid non-repudiation services. Data reports are analysed where it is stored within the application to indicate whether the state is in a normal/abnormal condition, and then, the alert messages/calls are sent to the parents/guardian.