Arif Wicaksana Oyong
Papers
2
Total Citations
22
H-Index
2
About
Arif Wicaksana Oyong’s research lies at the intersection of rehabilitation robotics and biomedical signal processing, with a core focus on restoring motor function for stroke survivors. His major contributions center on developing intuitive, human-robot interfaces that translate electromyography (EMG) signals into actionable control commands for assistive devices. In his most-cited work, “Robot assisted stroke rehabilitation: Estimation of muscle force/joint torque from EMG using GA” (2010, 15 citations), Oyong pioneered a genetic algorithm-based method to convert raw EMG data into accurate torque estimates, enabling smoother, more responsive robot-assisted therapy. He further refined this approach in “Estimation of muscle forces and joint torque from EMG using SA process” (2010, 7 citations), introducing simulated annealing to optimize signal-to-torque conversion. By tackling the fundamental challenge of decoding neural intent from muscle activity, Oyong’s work has directly advanced the practicality of EMG-driven rehabilitation robots, offering a pathway toward personalized, adaptive therapy. His contributions are particularly notable for bridging the gap between raw biosignals and real-time robotic control, a critical step in making stroke rehabilitation more effective and accessible.
Research Focus
Key Achievements
Top Papers
- 1
- 2Estimation of muscle forces and joint torque from EMG using SA process7 citations · 2010