Papers
2
Total Citations
15
H-Index
2
About
Xiaohu Ao is a researcher at the forefront of human-robot interaction and rehabilitation engineering, with a focus on making deep learning models more transparent and trustworthy. His primary research areas include surface electromyography (sEMG) signal processing, explainable artificial intelligence (XAI), and intelligent rehabilitation robotics. Ao’s major contribution lies in bridging the gap between high-performance deep learning models and clinical interpretability. His most cited work, “Explainable deep learning for sEMG-based similar gesture recognition: A Shapley-value-based solution” (2024, 13 citations), pioneers the use of Shapley values to demystify how neural networks distinguish between subtle, similar hand gestures—a critical challenge for prosthetic control. In a related study (2023, 2 citations), he introduced interpretable analysis of feature importance and implicit correlations using sEMG grayscale images, revealing hidden signal patterns that improve rehabilitation robot training. By converting raw sEMG signals into visual representations, Ao enables clinicians to understand which muscle features drive robotic assistance, directly enhancing therapy for upper-limb patients. His work is notable for advancing both the accuracy and accountability of AI in medical devices, making him a key figure in the push toward safe, human-centered rehabilitation technology.
Research Focus
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Top Papers
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