Papers
2
Total Citations
16
H-Index
2
About
Kaifan Zou is a researcher at the forefront of human-robot interaction and wearable robotics, specializing in the prediction and estimation of human motion from surface electromyography (sEMG) signals. His work focuses on developing intelligent algorithms that enable wearable devices—such as exoskeletons for soldiers, workers, or rehabilitation patients—to intuitively understand and respond to human intent. In his most cited paper (2022, 12 citations), Zou pioneered a novel method combining independent component analysis with support vector regression to predict knee trajectory from sEMG, addressing a critical challenge in real-time, non-invasive motion control. He further advanced the field in 2023 by introducing multiple kernels relevance vector regression for estimating knee joint angle, significantly improving model simplicity and human-robot perceptual viability. Though early in his career, Zou’s contributions are already shaping the next generation of adaptive, bio-signal-driven wearable robots. His work stands out for its practical focus on reducing computational complexity while enhancing prediction accuracy—a key step toward seamless, intuitive human-machine collaboration in demanding real-world environments.
Research Focus
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Top Papers
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