Machine Learning
Machine Learning
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Artificial intelligence enhances biomechanical analysis by enabling precise gait prediction, fracture risk assessment, and rehabilitation optimization. Our lab develops AI-driven models to analyze movement patterns, detect abnormalities, and improve mobility-related interventions in clinical and real-world settings.
On Going Works
Development of Pressure Ulcer Prevention Ultra-Low Noise Type Posture Change Care Robot
Development of an advanced air-cell mattress to reduce pressure on areas prone to pressure ulcers
Integration of sensor and vision data processing with deep learning-based analysis
Research and development of an algorithm to predict the risk of pressure ulcer occurrence in targeted areas
Optimization of posture change timing based on body pressure and position data to relieve pressure in ulcer-prone areas
Development of pressure distribution technology linked to care robots, including a sensor-based weight
Related Publications
Fracture risk assessment from 2D-DXA scan by supervised machine learning modeling
Heesun Choi, Luca Quagliato, Yoon-Sok Chung, Taeyong Lee
Synthetic Data-Empowered Supervised Machine Learning Modeling for a Binary Fracture Risk Assessment from 2D-DXA Analysis
Heesun Choi, Luca Quagliato, Yoon-Sok Chung, Taeyong Lee
Single and Multiple Gate Design Optimization Algorithm for Improving the Effectiveness of Fiber Reinforcement in the Thermoplastic Injection Molding Process
Mattia Perin, Youngbin Lim, Guido A. Berti, Taeyong Lee, Kai Jin, Luca Quagliato
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