Exploration of Adaptive sEMG Denoising and Deep Learning Paradigms and Their Application in Lower Limb Motion Recognition. - Xiaoyun Wang 006; Changhe Zhang; Zidong Yu; Chao Deng (M2VIP, 2024)

End-to-End Hand Gesture Recognition Based on Dynamic Graph Topology Generating Mechanism and Weighted Graph Isomorphism Network. - Zidong Yu; Changhe Zhang; Xiaoyun Wang 006; Chao Deng (M2VIP, 2024)

Parallel Graph Neural Networks: Class-Imbalanced Gait Phase Recognition via Adaptive Fusion of Multimodal sEMG and Kinematic Data. - Changhe Zhang; Xiaoyun Wang 006; Zidong Yu; Chao Deng (M2VIP, 2024)

Decoding of lower limb continuous movement intention from multi-channel sEMG and design of adaptive exoskeleton controller. - Xiaoyun Wang 006; Changhe Zhang; Zidong Yu; Chao Deng (Biomed. Signal Process. Control., 2024)

Exploration of deep learning-driven multimodal information fusion frameworks and their application in lower limb motion recognition. - Changhe Zhang; Zidong Yu; Xiaoyun Wang 006; Ze-Jian Chen; Chao Deng; Sheng Quan Xie (Biomed. Signal Process. Control., 2024)

Dynamic graph topology generating mechanism: Framework for feature-level multimodal information fusion applied to lower-limb activity recognition. - Zidong Yu; Changhe Zhang; Xiaoyun Wang 006; Chao Deng; Yuan Liu; Zeyu Yu (Eng. Appl. Artif. Intell., 2024)

Interpretable Dual-branch EMGNet: A transfer learning-based network for inter-subject lower limb motion intention recognition. - Changhe Zhang; Xiaoyun Wang 006; Zidong Yu; Bingjin Wang; Chao Deng (Eng. Appl. Artif. Intell., 2024)

Temporal-constrained parallel graph neural networks for recognizing motion patterns and gait phases in class-imbalanced scenarios. - Changhe Zhang; Zidong Yu; Xiaoyun Wang 006; Ze-Jian Chen; Chao Deng (Eng. Appl. Artif. Intell., 2025)

Lower limb Locomotion Mode Recognition based on sEMG: A pilot study in healthy individuals and stroke patients. - Changhe Zhang; Zidong Yu; Xiaoyun Wang 006; Ze-Jian Chen; Chao Deng (Biomed. Signal Process. Control., 2026)