Special Session Ⅷ

AI and Edge Intelligence for Gait Analysis and Rehabilitation Robotics

Chair		Co-Chair
Yan Wang Zhongyuan University of Technology, China		Hongnian Yu Edinburgh Napier University, UK

Keywords: Gait Analysis, Robotic Rehabilitation, Multi-Source Data Fusion, Edge Intelligence, Wearable Sensing, Human–Robot Interaction

Special Session Information: 

This session focuses on recent advances in AI-enabled gait analysis and rehabilitation robotics, with particular emphasis on edge-intelligent multi-source data fusion. The rapid development of wearable sensors, IoT platforms, and AI-driven biomechanical analytics is enabling the integration of diverse data stream—including motion capture, inertial sensing, electromyography signals, and plantar pressure measurements— to achieve more accurate movement characterization and adaptive rehabilitation strategies.

The session will explore recent advances in data fusion algorithms, multimodal sensing frameworks, and edge-deployable intelligent systems designed to enable real-time feedback, personalized rehabilitation, and efficient deployment on resource-constrained hardware. Through discussions on emerging technologies and case studies, this session aims to foster interdisciplinary collaboration across biomechanics, robotics, machine learning, and healthcare engineering.

Topics of interest include but are not limited to:

• Multi-source sensing and data fusion for gait analysis

• Robotic and wearable rehabilitation systems

• Edge intelligence and on-device AI deployment

• Human-robot interaction and adaptive control

• Federated and privacy-preserving learning for healthcare systems

• Intelligent sensing technologies for motion capture

• Cloud–edge collaboration for real-time rehabilitation feedback

• Deep learning and biomechanical modeling in gait assessment

• Digital twin and human-in-the-loop rehabilitation frameworks

• Evaluation and benchmarking of gait and rehabilitation datasets