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About

Sumantri R. Kurniawan is a researcher specializing in biomedical engineering, robotics, and human-machine interaction, with a particular focus on electromyography (EMG) signal processing and machine learning. His work centers on developing intuitive control systems for prosthetic and robotic hands, leveraging EMG signals from forearm muscles to classify hand gestures. In his most-cited study (2018, 19 citations), Kurniawan combined MYO Armband sensors with a Neural Network algorithm using Root Mean Square features to control a robot hand, achieving a learning rate of 0.7. He further advanced gesture classification by applying Principal Component Analysis and Support Vector Machines (2022, 9 citations), and compared time-domain versus frequency-domain EMG features for recognition (2021, 3 citations). Beyond prosthetics, Kurniawan has contributed to real-time object recognition for autonomous robotics, using YOLO deep neural networks to detect football field landmarks (2019, 5 citations). His work bridges signal processing, neural networks, and robotics, with applications in assistive technology and autonomous systems. Kurniawan’s research continues to impact the development of more responsive and natural human-machine interfaces.