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About

Zhihao Yang is a researcher at the forefront of wearable robotics and human motion analysis, with a primary focus on gait recognition and prediction for exoskeleton control. His work addresses a critical challenge in assistive robotics: enabling exoskeletons to accurately and in real-time understand a user's walking state to provide seamless, adaptive support. Yang's major contributions lie in developing advanced deep learning architectures that integrate spatial and temporal attention mechanisms with graph convolutional networks. Notably, his 2024 paper on "Spatial and temporal attention embedded spatial temporal graph convolutional networks for skeleton based gait recognition with multiple IMUs" (6 citations) pioneers the use of human skeletal structure from inertial measurement units (IMUs) to improve recognition accuracy. He has also innovated with MiniRocket-based methods for robust fine-grained gait recognition (3 citations) and auto-correlation enhanced graph networks for precise gait phase prediction (3 citations). By moving beyond raw inertial data to model the interconnectedness of human joints, Yang's work directly enhances the real-time control capabilities of lower-limb exoskeletons, making him a key contributor to the next generation of intelligent, responsive assistive devices.