Title: Exploring Advanced Training Facilities for Stroke Rehabilitation: Soft Robotics, Motion Analysis and Bilateral Treadmill Design
Abstract: In this seminar, we develop the latest advancements in training facilities for rehabilitation, focusing on soft robotics, motion analysis and balance. Our team is currently developing a novel AR-based Bilateral Treadmill Training System, which we believe will be of great interest to the sports science community. Our system features several innovative components, including a markerless and real-time motion capture system that allows for precise motion analysis without the need for markers. The bilateral belt treadmill supports different speed ratios between belts, enabling varied training scenarios. Additionally, the Femto Mega depth sensor camera requires only one or two cameras for the entire training system, enhancing efficiency. The markerless gait analysis is applicable to any treadmill with embedded force sensors, providing comprehensive gait analysis. Real-time monitoring and visual feedback offer immediate biomechanical feedback to athletes, while comprehensive gait pattern analysis includes both kinematic and kinetic data for detailed assessments. Motor and balance control assessments evaluate athletes’ motor skills and balance.
We see significant potential for applying this system in rehabiliation training. The bilateral belt design allows for a range of speeds and sudden movements, making it suitable for various training intensities. Our preliminary data from stroke rehabilitation training demonstrates immediate training effects and improvements in balance with soft robotic training, as evidenced by changes in the center of pressure (COP) after ten sessions. Bio: Zeng-Guang Hou is a Professor and Deputy Director of the State Key Laboratory
of Management and Control for Complex Systems, Institute of Automation, Chinese Academy
of Sciences (CAS). He is a VP of Chinese Association of Automation (CAA), VP of the Asia
Pacific Neural Network Society (APNNS). Dr. Hou is a CAA/ IEEE Fellow. He also serves as an
AE of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on
Cognitive and Developmental Systems, and an Editorial Board Member of Neural Networks.
Dr. Hou was a recipient of IEEE Transactions on Neural Networks Outstanding Paper Award
in 2013, and the National Natural Science Award of China in 2017, the Outstanding
Achievement Award of Asia Pacific Neural Network Society (APNNS) in 2017, and the Dennis
Gabor Award of International Neural Network Society (INNS) in 2023.
