Papers
1
Total Citations
5
H-Index
1
About
Cuisi Ou is a rising researcher in human-computer interaction and biomedical signal processing, with a particular focus on gesture recognition using multimodal physiological signals. Their most-cited work, "Study on Gesture Recognition Method with Two-Stream Residual Network Fusing sEMG Signals and Acceleration Signals" (2024), addresses a critical limitation in traditional machine learning approaches: the difficulty of manual feature selection for surface electromyography (sEMG) signals. By proposing a two-stream residual network architecture that fuses sEMG and acceleration data, Ou introduces a deep learning solution that captures complex, nonlinear relationships in the data, significantly improving gesture recognition accuracy. This work has already garnered 5 citations shortly after publication, signaling growing interest in their innovative fusion strategy. Ou’s contributions are particularly relevant for advancing intuitive, non-invasive human-computer interfaces, with potential applications in prosthetics, virtual reality, and assistive technologies. Their research stands at the intersection of signal processing and deep learning, offering a promising path toward more robust and natural interaction systems.
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