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About

Arif Wicaksono Oyong is a robotics and biomedical engineering researcher whose work centers on robot-assisted stroke rehabilitation, human upper limb biomechanics, and electromyography (EMG)-based human-machine interfaces. His pioneering research, beginning around 2009, tackled the complex challenge of modeling human arm kinematics — encompassing glenohumeral joints across abduction-adduction, flexion-extension, and rotational axes — to inform the development of intelligent rehabilitation robotic systems. Among his most influential contributions is his work on converting raw EMG signals into joint torque and force data, employing advanced optimization techniques such as Genetic Algorithms (GA) and Simulated Annealing (SA), as well as sliding mode control within EMG-driven musculoskeletal models. These approaches addressed a critical limitation of conventional rehabilitation robots: their reliance on pre-programmed trajectories that suppress a patient's voluntary movement. His research into AI techniques for estimating muscle force further expanded the toolkit available to rehabilitation engineers. Accumulating over 50 citations across his key publications, Oyong's body of work has made meaningful contributions to the design of more responsive, patient-adaptive rehabilitation technologies, offering promising pathways toward improved motor recovery outcomes for stroke survivors.