Emily Case

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About

Emily Case’s research lies at the intersection of neurorehabilitation, robotics, and human-computer interaction, with a focus on restoring motor function after stroke. Her most influential work, “Arm control recovery enhanced by error augmentation” (2011, 38 citations), introduced a groundbreaking paradigm: using haptic and graphic distortions to amplify movement errors during therapy, thereby accelerating motor learning in chronic stroke survivors. This study demonstrated how a trio of patient, therapist, and machine could work in concert, leveraging massed practice and augmented feedback to drive neural recovery. Case’s second key contribution, “Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library” (2011, 8 citations), addressed a critical barrier in the field—the lack of standardized software interfaces for diverse robotic systems. By creating a unified virtual environment library, she enabled seamless integration of haptic and graphic biofeedback across platforms, making rehabilitation technology more accessible and reproducible. Her work has been pivotal in shifting stroke therapy from passive, repetitive exercises to adaptive, error-driven learning. Though early in her career, Case’s innovative fusion of robotics and neuroscience has already shaped how clinicians and engineers design interactive rehabilitation tools, promising more personalized and effective recovery pathways for millions of stroke survivors worldwide.