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About

Alison H. McGregor’s research lies at the intersection of rehabilitation robotics, sensor fusion, and human motion analysis, with a focus on translating engineering advances into tangible clinical outcomes. Her most influential work, a 2019 paper with 75 citations, introduces a refined gradient descent MARG orientation algorithm that enhances accuracy and robustness over the widely-used Madgwick filter—critical for applications like robot teleoperation and wearable inertial sensing. In rehabilitation, she has pioneered control strategies for gait robots, proposing four distinct assistant control modes based on EMG evaluation (27 citations) to tailor therapy to individual patient needs and stages of recovery. Her investigations into body-weight supported locomotion training (6 citations) further illuminate how robotic-aided unloading modulates lower limb muscle activity, directly informing the design of overground rehabilitation systems. McGregor also bridges the gap between clinical assessment and engineering design, as seen in her work on hand strength evaluation for task-oriented robotic rehabilitation (2 citations). By grounding technical innovation in real-world therapeutic challenges, her research has advanced both the precision of sensor algorithms and the personalization of robotic rehabilitation, making her a key figure in the development of intelligent, patient-responsive assistive technologies.