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About

Sumantri Kurniawan Risandriya is a researcher advancing the field of human-robot interaction through electromyography (EMG)-based control systems. His work focuses on interpreting muscle signals to enable intuitive prosthetic and robotic hand control, bridging the gap between biological movement and machine response. In his most cited paper (2019, 9 citations), he introduced a method using FFT as input to a Neural Network algorithm, leveraging Myo Armband sensors to decode muscle activity for prosthetic control. He further refined gesture recognition by comparing K-NN and Naïve Bayes classifiers (2020, 6 citations), demonstrating the potential of machine learning in EMG signal processing. His research achieved up to 87.5% accuracy in mimicking finger movements (2020), and he later employed a PSO-optimized SVM algorithm (2022) to classify individual finger movements with greater precision. By systematically exploring neural networks, pattern recognition, and optimization techniques, Risandriya contributes to more responsive, accessible prosthetic technologies. His work holds promise for improving the quality of life for amputees and advancing the broader field of bio-signal-driven robotics.