Giorgia Lupinacci

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About

Giorgia Lupinacci’s research sits at the intersection of rehabilitation engineering and medical robotics, with a focus on improving patient outcomes through precise, data-driven technologies. Her most cited work, “The Toronto Rehab Stroke Pose Dataset to Detect Compensation During Stroke Rehabilitation Therapy” (2017, 49 citations), addresses a critical challenge in stroke recovery: the detection of compensatory movements. When stroke survivors use unaffected joints to complete tasks, it can undermine the effectiveness of rehabilitation exercises. Lupinacci’s dataset provides a foundational tool for developing automated systems that identify these patterns, enabling more targeted and effective therapy. In parallel, her work on the Navi-Robot system (2017, 6 citations) demonstrates her expertise in surgical robotics. This study validated a robotic guidance system for CT-guided needle biopsies, aiming to reduce radiation exposure and procedure time while maintaining high precision. Together, these contributions highlight Lupinacci’s commitment to creating practical, clinically relevant technologies—from rehabilitation monitoring to minimally invasive interventions—that directly enhance patient care and therapeutic precision.