Maria Fonoberova

Papers

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About

Maria Fonoberova is a leading researcher at the intersection of human-machine interaction and rehabilitation robotics, with a primary focus on restoring hand function through intelligent, adaptive systems. Her most-cited work, "Koopman-Driven Grip Force Prediction Through EMG Sensing" (2025, 2 citations), introduces a novel approach that leverages Koopman operator theory to predict grip force from surface electromyography (sEMG) signals. This contribution is pivotal for robotic rehabilitation, enabling devices to dynamically adjust force output based on real-time muscle activity—a critical advancement for patients with conditions like stroke or multiple sclerosis. By bridging nonlinear dynamics and biosignal processing, Fonoberova’s research enhances the responsiveness and personalization of assistive technologies. Her work not only advances the field of rehabilitation engineering but also holds promise for broader applications in prosthetics and human augmentation. With a growing citation footprint, Fonoberova is establishing herself as a key innovator in data-driven, patient-centered robotic systems, offering new pathways for restoring independence and quality of life.