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About

Xiaorong Guan is a multidisciplinary researcher whose work spans human-machine interfaces, computer vision, and wearable robotics, with particular expertise in surface electromyography (sEMG)-based motion prediction and multi-view stereo (MVS) reconstruction. Guan's most influential contribution, "sEMG-Based Lower Limb Motion Prediction Using CNN-LSTM with Improved PCA Optimization Algorithm" (2022, 93 citations), established a robust framework for decoding neuromuscular signals to predict lower limb motion — a breakthrough with direct implications for exoskeleton control and rehabilitation engineering. Complementary sEMG studies employing independent component analysis and support vector regression further demonstrate Guan's systematic approach to advancing neural decoding for wearable assistive devices. In parallel, Guan has made notable strides in 3D scene reconstruction, developing pixel-visibility learning methods with cost aggregation and regularization for MVS — work that collectively has garnered over 80 citations and holds promise for autonomous driving and robotic navigation. A 2024 paper on edge-assisted epipolar transformers (39 citations) reflects Guan's continued innovation in industrial scene understanding. A systematic review of wearable extra robotic limbs rounds out a research portfolio that is both technically rigorous and deeply oriented toward real-world human augmentation applications.