Daniel Sutopo

Papers

Total Citations

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About

Daniel Sutopo is a researcher whose work bridges biomedical signal processing and human-computer interaction, with a particular focus on electromyography (EMG)-based gesture recognition. His most-cited paper, "EMG Based Classification of Hand Gesture Using PCA and SVM" (2022), has garnered 9 citations, demonstrating its relevance in the field. In this work, Sutopo developed a robust framework that combines Principal Component Analysis (PCA) for dimensionality reduction with Support Vector Machines (SVM) for accurate classification of hand gestures from EMG signals. This contribution is significant for advancing prosthetic control and assistive technologies, offering a computationally efficient method that enhances real-time performance. Sutopo’s research addresses key challenges in pattern recognition, including noise reduction and feature extraction, making his work a valuable resource for students and engineers developing intuitive human-machine interfaces. His achievements highlight a commitment to translating complex biological signals into practical applications, and his citation record reflects growing interest in his methodologies. For those exploring EMG-based systems, Sutopo’s work provides a clear, impactful example of how machine learning can unlock new possibilities in rehabilitation and robotics.