Maryam Mashayekhi

Papers

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About

Maryam Mashayekhi is a researcher at the intersection of rehabilitation robotics, human-robot interaction, and neuromuscular control. Her primary contributions lie in developing adaptive control strategies for hand rehabilitation robots, with a focus on mitigating muscle fatigue during therapy. Her most cited work, "EMG-driven fatigue-based self-adapting admittance control of a hand rehabilitation robot" (2022, 24 citations), introduces a novel framework that uses electromyographic (EMG) signals to dynamically adjust robotic assistance based on real-time fatigue levels. This approach addresses a critical limitation in rehabilitation: constant task difficulty can exacerbate muscle fatigue, reducing therapy effectiveness. By enabling robots to adapt their admittance in response to prolonged muscle activity, Mashayekhi’s work enhances patient comfort and promotes sustained engagement during repetitive hand exercises. Her earlier study (2019) laid the groundwork for this fatigue-adaptive control, highlighting the neuromuscular system’s decline in force production under sustained load. Mashayekhi’s research has significant implications for personalized neurorehabilitation, offering a pathway to more intelligent, patient-responsive robotic therapy. Her work is essential reading for those developing assistive technologies that prioritize both efficacy and user well-being.