IoT-Enabled Smart E-Healthcare System with Predictive Prescription Algorithm for Automatic Patient Monitoring and Treatment

Project Code :TEMBMA3652

Objective

This study develops an IoT-enabled smart e-healthcare system with a predictive prescription algorithm for automatic patient monitoring and treatment, aiming to improve healthcare efficiency and provide personalized care.

Abstract

The " IoT Enabled Smart E Healthcare System with Predictive Prescription Algorithm for Automatic Patient Monitoring and Treatment" is designed to continuously track and monitor critical health parameters of patients, providing real-time data to caregivers remotely. The system integrates a range of sensors, including a heart rate sensor to monitor heartbeat, a respiratory sensor for breathing patterns, a MEMS sensor to detect abnormal walking or posture, and a Dallas temperature sensor (DS18B20) to measure body temperature. Uses Machine learning random forest algorithm for sensor data processing to give predictive alerts.With the support of GPS and GSM modules, the system enables location tracking and sends health alerts via SMS in case of abnormal conditions. An Arduino UNO serves as the central controller, processing sensor data and displaying real-time information on an LCD. The system is powered by a 5V power supply and 12V adapters, ensuring consistent operation. By leveraging IoT technologies, this system enhances patient care through continuous remote monitoring, reducing the need for in-person supervision while allowing timely intervention during medical emergencies.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

- Heartbeat Sensor
- Respiratory Sensor
- MEMS Sensor
- Dallas Temp Sensor
- GPS
- GSM
- LCD
- 5V Power Supply
- 12V 1A Adapter
- Arduino UNO - Heartbeat Sensor
- Respiratory Sensor
- MEMS Sensor
- Dallas Temp Sensor
- GPS
- GSM
- LCD
- 5V Power Supply
- 12V 1A Adapter
- Arduino UNO

Learning Outcomes

Understanding the pin diagram and architecture of Arduino UNO.
Installing and setting up the Arduino IDE.
Configuring and connecting Arduino UNO with sensors and modules.
Basic coding and programming with Arduino UNO.
Working with health monitoring sensors like heart rate, respiratory, MEMS, and temperature sensors.
Interfacing GPS and GSM modules with Arduino.
Using LCDs to display real-time health data.
Understanding the power requirements for Arduino.
Introduction to using machine learning for predictive alerts in IoT systems.