Overview
Course Background
Big data analysis and smart rehabilitation have become key trends in modern rehabilitation medicine. By leveraging advanced computing technologies, artificial intelligence (AI) algorithms, and Internet of Things (IoT) devices, healthcare professionals can assess patients’ functional status more accurately, optimize rehabilitation plans, and implement personalized interventions. This course focuses on the application of big data in rehabilitation medicine, helping learners master data-driven approaches to improve rehabilitation outcomes.
Course Content
This course consists of the following core modules:
Fundamentals of Big Data Analysis – Data collection, preprocessing, statistical analysis methods, and visualization techniques.
Artificial Intelligence in Rehabilitation – Machine learning, deep learning, and their applications in gait analysis and neurorehabilitation.
Smart Rehabilitation Devices – Wearable technologies, remote rehabilitation systems, and virtual reality (VR)-based training.
Clinical Case Studies – Real-world rehabilitation cases demonstrating the integration of big data techniques for clinical decision-making.
Future Trends in Smart Rehabilitation – Emerging developments and the integration of intelligent rehabilitation technologies into healthcare systems.
Course Objectives
Understand the fundamental principles of big data analysis and its applications in rehabilitation medicine.
Learn how to operate and evaluate smart rehabilitation devices for clinical use.
Enhance data-driven decision-making skills in rehabilitation research and practice.
Explore innovative AI-powered rehabilitation strategies to improve patient outcomes.
This course is designed to bridge the gap between data science and rehabilitation medicine, providing participants with the knowledge and tools needed to harness big data for smarter, more efficient rehabilitation interventions.
