Personalized Blood Pressure Control by Machine Learning for Remote Patient Monitoring
Objective
This study focuses on developing a machine learning-based system for personalized blood pressure control, enabling effective remote patient monitoring and tailored management to improve patient outcomes.
Abstract
The "Personalized Healthcare Delivery through Smart Wearable Technology" project aims to enhance individual health management by integrating various sensors with a Raspberry Pi to monitor vital health parameters in real-time. Utilizing a blood pressure sensor, temperature sensor, pulse oximeter, and respiratory sensor, this system captures critical health data continuously. An ADC module ensures accurate data conversion for effective monitoring. Machine learning, specifically a random forest algorithm, analyzes the sensor readings to detect abnormalities, triggering alerts displayed on an LCD. For instance, if abnormal blood pressure is detected, the system prompts the user to take their medication. Furthermore, the collected data is uploaded to ThingSpeak, facilitating remote health monitoring and analysis. This innovative approach not only empowers users to manage their health proactively but also provides healthcare professionals with valuable insights into patients' health trends, ultimately leading to personalized care and improved health outcomes.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
- - Raspberry Pi
- - Memory card
- - Blood pressure sensor
- - Temperature sensor
- - Pulse oximeter
- - Respiratory sensor
- - ADC module
- - LCD
Learning Outcomes
- - Understanding Raspberry Pi architecture and pin diagram
- - Installing Raspberry Pi OS and necessary software
- - Setting up and configuring Raspberry Pi for sensor integration
- - Introduction to Raspberry Pi programming and software tools
- - Basic coding with Python for sensor data processing
- - Working with various health sensors (blood pressure, temperature, etc.)
- - Data acquisition and interfacing with an ADC module
- - Analyzing sensor data using machine learning algorithms
- - Uploading and managing health data on ThingSpeak
- - Understanding power supply requirements for Raspberry Pi and sensors
- About Project Development Life Cycle:
- Planning and Requirement Gathering (software’s, Tools, Hardware components, etc.,)
- Schematic preparation
- Code development and debugging
- Hardware development and debugging
- Development of the Project and Output testing
- Practical exposure to:
- Hardware and software tools.
- Solution providing for real time problems.
- Working with team/ individual.
- Work on Creative ideas.
- Project development Skills
- Problem analyzing skills
- Problem solving skills
- Creativity and imaginary skills
- Programming skills
- Deployment
- Testing skills
- Debugging skills
- Project presentation skills
- Thesis writing skills
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
