Capturing Motion,
Crafting Stories

标题：Innovative Applications in Healthcare Biorobotics: Injury Prevention, AR Training, and VR Rehabilitation

Dr. Liang He is the Associate Professor in Department of Engineering Science at University of Oxford, where he leads the Oxford Healthcare Biorobotics Lab.

The research team utilizes NOKOV motion capture to achieve accurate kinematic data, such as portable motion tracking systems for sports injury prevention using biomechanical modeling; robotic patient simulators for medical training with AR; and VR for medical rehabilitation.

Sports Injury Prevention

This research is dedicated to developing and testing portable motion tracking systems and establishing biomechanical modeling for the monitoring and prevention of sports injuries. By analyzing motion models, it predicts high-risk movement patterns that could lead to injuries. For instance, it forecasts how certain specific gymnastic training movements might impact the wrist joints over the long term. NOKOV Motion Capture provides accurate laboratory kinematic data, aiding in the design and analysis of new biomechanical models and portable motion tracking systems.

Medical AR Training

A specific exploration area of this research is the development of robotic patient simulators and training intelligence for medical training and patient care. By combining soft robotics with augmented reality technology, it can realistically reproduce the physiological and pathological conditions of the human body, allowing medical students and professionals to practice procedures and decision-making in a controlled, risk-free environment. NOKOV Motion Capture is used in this research to measure the operational movements, dexterity, and success rates of experts and medical students. With these technologies combined, the simulator can provide real-time feedback, analyze trainee performance, and offer personalized improvement suggestions, ultimately enhancing medical training effectiveness and patient care quality.

VR Sports Training

The goal of this research is to explore how to combine motor learning and sensory-motor coordination theories with virtual reality (VR) devices for specific medical rehabilitation and training fields, including stroke recovery, sports physiotherapy, and sports skill practice. Modeling based on a large amount of behavioral data, combined with haptic interaction media of soft robots and VR devices, provides controllable tasks, environments, and rewards for this type of training. NOKOV Motion Capture lays the foundation for collecting movement data in behavioral datasets.