Andrivo Rusydi

Papers

Total Citations

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About

Andrivo Rusydi is a leading researcher in the fields of assistive robotics and biomedical signal processing, with a focus on developing intuitive human-machine interfaces. His major contributions center on hybrid control systems that integrate flex sensors and electromyography (EMG) signals to enable natural, non-invasive robot operation. Notably, his 2018 work on a flex sensor and EMG hybrid control system for a robot arm—designed to assist individuals with special needs—has garnered 15 citations, demonstrating its impact on accessible robotics. Rusydi’s research explores alternative control methods, such as using jaw, arm, and leg muscle contractions to command mobile robots, as seen in his 2019 study employing artificial neural networks for EMG-based control. By analyzing muscle activity patterns, he has advanced the development of adaptive, user-friendly robotic systems that respond to human intent. His work bridges engineering and rehabilitation, offering practical solutions for people with motor impairments. Rusydi’s innovative approach to combining sensor technologies and machine learning continues to shape the future of assistive robotics, making him a notable figure in the field.