The content discusses the fusion of electromyography (EMG) and vision data for improved grasp intent inference in prosthetic hand control. It highlights challenges with current control methods based on physiological signals and the potential benefits of multimodal evidence fusion. The study presents a Bayesian evidence fusion framework, novel data processing techniques, and experimental results demonstrating enhanced accuracy through fusion.
The study emphasizes the importance of additional sources of information to provide more robust control of robotic hands. It explores the complementary strengths of EMG and visual evidence, showcasing how fusion can outperform individual modalities. The research aims to improve real-world scenarios by considering dynamic protocols and diverse datasets for comprehensive analysis.
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Overall, the study provides insights into advancing prosthetic hand control through innovative fusion techniques.
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arxiv.org
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by Mehrshad Zan... at arxiv.org 02-29-2024
https://arxiv.org/pdf/2104.03893.pdfDeeper Inquiries