Papers
1
Total Citations
3
H-Index
1
About
C. Lhoste is a researcher advancing the field of human–machine interaction, with a primary focus on electromyography (EMG)-based control systems and deep learning. Their most-cited work, "EMG Data Augmentation for Grasp Classification Using Generative Adversarial Networks" (2022, 3 citations), tackles a critical bottleneck in prosthetic and robotic hand control: the scarcity of high-quality EMG training data. By introducing generative adversarial networks (GANs) for data augmentation, Lhoste demonstrates how synthetic EMG signals can significantly improve grasp classification accuracy, enabling more robust and intuitive control of robotic hands. This contribution addresses a long-standing challenge in the field—the variability and limited availability of real-world EMG recordings—and opens new possibilities for commercial applications in assistive technology and embedded systems. Lhoste’s work bridges the gap between traditional signal processing and modern deep learning, offering practical solutions for real-time, low-latency decoding. With a focus on translational impact, their research is poised to influence next-generation prosthetic devices and human–robot interfaces, making natural, dexterous control more accessible to users.
Research Focus
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Top Papers
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