International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 12 | Dec -2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 490 Smart (scalable medical alert response technique) Health Monitoring System Using Raspberry Pi Altaf Mulla, Prof.Tushar Mote ME student of JSCOE collage Pune, Maharashtra, India Assistant Professor of Entc Dept. JSCOE collage Pune, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - An As saying a DzHealth is wealthdz is exceptionally crucial to make utilization of innovation for better well being. Smart health monitoring systems using raspberry pi have been given more attention in recent research efforts as they are not only used for patients but also recommended for old age people, sports persons and home makers. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. The self health care monitoring system aims at monitoring the condition of the vital organs in patients continuously at home. In this project is used as a gateway to communicate to the various sensors such as temperature sensor and accelerometer sensor. In medicine and biotechnology, sensors are tools that detect specific biological, chemical or physical processes and then transmit or report this data monitoring vital signs. Keywords—Epilepsy; scalable medical Alert; Python shell, Respiration, pulse rate, temperature sensor 1. INTRODUCTION The SMART (Scalable Medical Alert Response Technology) system using raspberry pi integrates patient monitoring (body temperature, Pulse rate, ), geo-positioning, signal processing, targeted alerting, and a wireless interface for caregivers. Smartphone apps with self-monitoring and sensing capabilities can help in disease prevention; however, such context-aware applications are difficult to develop, due to the complexities of sensor data acquisition, context modeling, and data management .In this learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. The monitoring of vital physiological signals has proven to be one of the most efficient ways for continuous and remote tracking of the health status of patients. Wearable sensors monitors are often used to diagnose and monitor a person’s health status by measuring their cardiac activity. Wearable sensor such as temperature sensors, accelerometer using raspberry pi is monitor, which can be utilized to evaluate the body temperature activity, epilepsy detection, Position of comma patient. This procedure is very useful for monitoring people with (or susceptible to) impairments in their cardiac activity. An increase in world population along with a significant aging portion is forcing rapid rises in healthcare costs. The healthcare system is going through a transformation in which continuous monitoring of inhabitants is possible even without hospitalization. The advancement of sensing technologies using raspberry pi, makes it possible to develop smart systems to monitor activities of human beings continuously. Wearable sensors detect abnormal and/or unforeseen situations by monitoring physiological parameters along with other symptoms. Therefore, necessary help can be provided in times of dire need. This project will help the people that can monitor or check their health issue instead of going hospitals. People itself taking care with monitor their health and it is very simple method and very cost efficient .Fitness monitoring is a fundamental service in pervasive healthcare, but finding a balance between usability and privacy is a hard challenge. They demonstrate this idea with a fitness monitoring system for the healthy individuals in a workplace. The system maintains its original interface for users, in order to provide the same ease of usability. The system uses collected physiological information (body temperature, epilepsy detection and pulse rate) etc.