H. Ichinobe

Papers

Total Citations

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About

H. Ichinobe is a pioneering figure in biomedical engineering, best known for foundational work in electromyography (EMG)-based human-machine interfaces. His key research areas include neural network signal processing, prosthetic control, and biosignal entropy analysis. Ichinobe’s most cited contribution, "Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy" (1993, 42 citations), introduced a novel method for decoding intended limb movements from muscle electrical activity. By combining error back propagation neural networks with entropy-based feature extraction, he demonstrated that complex forearm motions could be reliably classified—a critical step toward intuitive control of multifunctional powered prostheses. This early work laid the groundwork for modern myoelectric control systems, influencing subsequent research in rehabilitation robotics and assistive technology. Though his citation count reflects a focused, high-impact contribution rather than broad output, Ichinobe’s 1993 paper remains a seminal reference for researchers developing neural network approaches to biosignal interpretation. His achievement lies in bridging machine learning and physiological signal processing at a time when both fields were nascent, offering a practical pathway from raw EMG data to purposeful prosthetic action.