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About

Albert Jin is a neuroscience and biomedical engineering researcher whose work sits at the intersection of robotics, neurological assessment, and clinical rehabilitation. His research focuses primarily on developing and applying robotic platforms to objectively quantify sensorimotor and cognitive impairments across a range of neurological conditions, including transient ischemic attack (TIA), multiple sclerosis, epilepsy, and stroke. Jin's most influential contribution — his 2017 paper on robotic exoskeleton assessment of TIA, which has garnered 42 citations — demonstrated that subtle upper limb motor abnormalities in TIA patients could be precisely captured using the Kinarm Exoskeleton robot, a tool far more sensitive than conventional clinical scales. This work laid the groundwork for a broader research program exploring the feasibility of robotic assessments in MS (19 citations) and epilepsy (14 citations), consistently revealing that robotics can detect behavioral nuances missed by gold-standard clinical tools. More recently, his 2025 work on evidential networks for stroke detection (12 citations) reflects a pivot toward integrating machine learning to further refine neurological evaluations. His longitudinal TIA study additionally highlights his commitment to tracking patient recovery over time. Collectively, Jin's research is reshaping how clinicians measure and monitor neurological disability with unprecedented precision.