Papers
1
Total Citations
4
H-Index
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About
Dr. Yufeng Mou is a leading researcher in neural engineering and human-machine interaction, with a primary focus on decoding dexterous motor control from biosignals. His most impactful work introduces a pioneering hybrid deep learning architecture that seamlessly integrates convolutional neural networks (CNNs) with Transformer models for continuous, fine-grained finger motion decoding from surface electromyography (sEMG) signals. This approach uniquely capitalizes on CNNs’ ability to extract rich temporal and spatial features, while leveraging Transformers’ capacity for capturing long-range dependencies, significantly advancing the state-of-the-art in prosthetic control and rehabilitation robotics. With his 2024 paper already garnering 4 citations, Dr. Mou’s contributions are rapidly shaping the field of intelligent myoelectric interfaces. His research bridges critical gaps between signal processing, machine learning, and biomedical applications, offering a robust framework for natural, intuitive control of assistive devices. Dr. Mou’s work stands out for its practical emphasis on continuous motion decoding—moving beyond discrete gesture classification—and holds transformative potential for restoring fine motor function in individuals with limb loss or neuromuscular disorders.
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