Knee Osteoarthritis Prediction and Progression
A unified decision-support system integrating medical imaging, clinical biomarkers, and wearable IoT sensors for early detection and long-term monitoring of Knee Osteoarthritis.
Research Domain
Bridging clinical assessment, medical imaging, and continuous patient monitoring through advanced AI.
Literature Survey
Analysis of AI in KOA diagnosis using clinical databases, blood biomarkers, and CNN-based radiographic assessment (Kellgren-Lawrence grading).
Research Gap
Conventional methods rely on single-modality info and lack continuous monitoring, making real-life progression tracking difficult.
Research Problem
High cost of MRI/X-ray and impracticality of frequent hospital visits for elderly patients requiring long-term monitoring.
Research Objectives
Developing low-cost, explainable monitoring systems using IoT sensors and multimodal data fusion for clinical decision support.
Methodology
Phased approach: Clinical prediction (XGBoost), Binary Classification (YOLOv8), Severity Grading (EfficientNetB0), and IoT Wearable Monitoring.
Technologies Used
Python, TensorFlow, YOLOv8, XGBoost, ESP32, MPU6050 (Vibration), and MLX90614 (Thermal) sensors.
Project Milestones
Following our journey from the initial proposal to the final assessment. Each milestone represents a key phase in our research, design, and development process.
Project Proposal
Initial project proposal presentation outlining the research problem, objectives, and feasibility analysis.
Progress Presentation 1
First progress review covering literature survey completion, system design, and initial prototype development.
Progress Presentation 2
Second progress review demonstrating ML model training results, system integration, and testing outcomes.
Final Assessment
Final project assessment with complete system demonstration, evaluation metrics, and documentation review.
Viva & Demonstration
Final viva voce examination presenting research contributions, findings, and future recommendations.
Downloads
Access our collection of documents and presentations available in PDF and PPTX format. Click on any file to download.
Proposal Documents
Check List Documents
Final Documents
No documents available yet.
Research Team
The innovators behind the KneeCare platform.
Jenny Krishara
Supervisor
Thamali Dassanayake
Co-supervisor
Fernando W.D.A
Full stack developer
IT22223708
Jayasinghe J.M.N.S.
Data Analyst
IT22582942
Perera B.B.A.R
ML Engineer
IT22606792
Gamage D.M.G.P.K
IoT Developer
IT22188472
Contact Us
For research collaborations or inquiries regarding the KneeCare system.