Project Definition:
University athletes often undergo intense physical training and competition, making real-time heart rate monitoring essential for optimizing performance, preventing injuries, and ensuring overall well-being. Traditional fitness tracking methods lack advanced AI-driven insights that can predict fatigue, overtraining, and potential cardiovascular risks. This project aims to develop an AI-powered heart rate monitoring system that collects and analyses real-time heart rate data from wearable IoT devices.

Research Goals:
• Develop an AI-based heart rate monitoring system that enhances athletic performance and safety.
• Implement IoT connectivity for real-time heart rate tracking and cloud-based analytics.
• Use machine learning models to identify patterns in heart rate variability (HRV), fatigue, and recovery rates.
• Design and 3D-print a custom, lightweight enclosure for seamless integration into sportswear or wearables.

Methodology:
o AI-Powered Heart Rate Monitoring System
o Integrate IoT-based heart rate sensors (e.g., chest straps, wristbands) for real-time data collection.
o Utilize AI and machine learning algorithms (e.g., Random Forest, LSTMs, CNNs) to analyse:
o Heart rate trends before, during, and after training.
o Signs of overtraining, dehydration, or cardiovascular strain.
o Optimal recovery periods and performance efficiency.

o Develop a mobile/web dashboard for athletes and coaches to visualize analytics.
o IoT-Based Connectivity & Cloud Analytics
o Deploy Bluetooth/WiFi-enabled wearables for data transmission.
o Implement cloud-based storage (AWS, Google Firebase) for real-time access to athlete health data.
o Provide automated alerts for abnormal heart rate fluctuations or early signs of exhaustion.
o 3D-Printed Wearable Enclosure Design
o Design a lightweight, durable, and sweat-resistant 3D-printed case for housing IoT sensors.
o Optimize for ergonomic placement (e.g., wrist, chest strap, ankle) to ensure accurate readings.
o Use 3D printing materials like TPU or flexible PLA for comfort and durability.
o Machine Learning & Performance Prediction

Train ML models on historical and real-time heart rate data to:
▪ Predict fatigue risk based on training intensity.
▪ Provide recommendations for rest periods and peak performance windows.

▪ Detect early warning signs of cardiovascular stress.

Project Objectives:
Objective 1: Develop a real-time AI-powered heart rate monitoring system for university athletes.
Objective 2: Implement an IoT-enabled data collection and cloud-based analytics platform.
Objective 3: Use machine learning to predict performance efficiency and fatigue levels.
Objective 4: Design and 3D-print a compact, durable enclosure for sports applications.