Elham Dolatabadi
Papers
2
Total Citations
118
H-Index
2
About
Elham Dolatabadi is a leading researcher at the intersection of artificial intelligence, rehabilitation robotics, and computational motor neuroscience. Her work focuses on developing intelligent systems that can automatically detect and quantify compensatory movements during stroke rehabilitation therapy—a critical challenge in ensuring effective recovery. In her seminal 2017 paper, "Automatic Detection of Compensation During Robotic Stroke Rehabilitation Therapy" (69 citations), she pioneered machine learning methods to identify when stroke survivors unconsciously recruit unaffected muscles and joints to compensate for impaired limbs, a behavior that can undermine rehabilitation outcomes if left unchecked. She further advanced the field by creating The Toronto Rehab Stroke Pose Dataset (49 citations), a publicly available benchmark that enables researchers worldwide to develop and validate algorithms for detecting these compensatory motions during upper limb therapy. This dataset has become a foundational resource in rehabilitation robotics. Dolatabadi's contributions are notable for bridging clinical rehabilitation needs with cutting-edge AI, offering practical tools to enhance therapy delivery and patient outcomes. Her work is essential reading for researchers in rehabilitation engineering, human-robot interaction, and applied machine learning.
Research Focus
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Top Papers
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