Muchamad Arif Hana Sasono

About

Muchamad Arif Hana Sasono is a rising researcher in biomedical engineering and human-machine interaction, with a focus on decoding complex physiological signals for assistive technologies. His work centers on electromyography (EMG)-based control systems, particularly for finger movement classification—a critical challenge for advancing exoskeleton robotics, prosthetic hands, and powered wheelchairs. In his most-cited paper, "Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband" (2023, 4 citations), Sasono addresses the difficulty of accurately decoding movements from EMG signals. He systematically evaluates feature extraction techniques and machine learning algorithms to improve classification performance, offering practical insights for real-time prosthetic control. Though early in his career, his contributions are already shaping how researchers approach robust, non-invasive interfaces. By tackling the bottleneck of signal interpretation, Sasono is helping bridge the gap between raw biological data and responsive, life-enhancing devices—work that holds promise for restoring mobility and independence to individuals with limb differences or paralysis.

Research Focus

Key Achievements

Top Papers

1. 1
   Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

   4 citations · 2023

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