Asanavision: Real-Time Yoga Posture Detection and Feedback Using Machine Learning
Objective
To design and develop a real-time yoga posture detection system using machine learning techniques for accurate pose recognition. To provide immediate feedback and guidance to users by analyzing body posture, thereby improving correctness, preventing injuries, and enhancing overall yoga practice efficiency.
Abstract
This project presents Asanavision: Real-Time Yoga Posture Detection and Feedback Using Machine Learning, designed to assist users in performing yoga accurately and safely. The system is built using a Raspberry Pi integrated with a USB web camera, MEMS sensor, force sensor, LCD display, buzzer, and speaker. The camera captures real-time video to detect and classify yoga postures using machine learning techniques.The MEMS sensor is used for fall detection, while the force sensor monitors body position and weight distribution during yoga practice. Based on the trained model, the system identifies different yoga poses and provides real-time feedback through a speaker by announcing the posture name. If any abnormal condition or incorrect posture is detected, the buzzer gives an alert. The LCD display shows posture information and system status.This system improves posture accuracy, ensures user safety, and enhances the overall yoga experience. It can be effectively used for personal fitness training, rehabilitation, and smart healthcare applications.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Hardware components:
Raspberry Pi
Memory Card
USB Web Camera
LCD Display
MEMS Sensor
Force Sensor
Buzzer
Speaker
Power Supply
Adapter
Software components:
Python
Rasbian OS
Learning Outcomes
- Understand Raspberry Pi architecture and GPIO configuration
- Learn how to install and configure Raspbian OS and required Python libraries
- Interface analog sensors with Raspberry Pi using MCP3008 ADC
- Implement image classification using Artificial Neural Networks
- Develop real-time skin analysis using USB camera input
- Build automated health screening systems with display and alert features
- Integrate temperature and heartbeat monitoring in diagnostic systems
- Analyze and interpret classification output for healthcare applications
- 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
Paper Publishing
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6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
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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.
