Wanyin Wu

Papers

Total Citations

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About

Wanyin Wu’s research advances intelligent robotics and human–machine interaction, with a primary focus on enhancing machine perception and motor control. Her most cited work, “Random Cropping Ensemble Neural Network for Image Classification in a Robotic Arm Grasping System” (2020, 42 citations), tackles a critical bottleneck in industrial automation: robust image classification of randomly placed parts. By introducing a novel ensemble strategy that leverages random cropping, Wu significantly improved classification accuracy under real-world, unstructured conditions—a key step toward more autonomous robotic manipulation. She also contributed the “HAR-sEMG: A Dataset for Human Activity Recognition on Lower-Limb sEMG” (2021, 18 citations), providing a valuable benchmark for decoding human movement from surface electromyography signals. This work supports the development of intuitive, wearable assistive devices and prosthetics. Through these contributions, Wu bridges the gap between computer vision and biomedical signal processing, demonstrating a clear impact on both industrial robotics and rehabilitation technology. Her work is shaping how machines see and respond to the physical world, with practical implications for smart manufacturing and human–robot collaboration.