Assistant Professor, Faculty of Engineering
Ami Drory is an assistant professor in the Faculty of Engineering. He earned a PhD in Engineering and Computer Science under Richard Hartley and Hongdong Li at one of the world's leading computer vision labs at the Australian National University (ANU). Through this work he developed techniques for markerless pose and surface geometry estimation, activity recognition and tracking for biomechanics applications. Prior to joining the University of Nottingham he was a Biomedical Engineering research fellow at the Centre for Bionic Medicine at the Department of Physical Medicine & Rehabilitation at Northwestern University, where he undertook research in assistive technology and using wearable sensors, computer vision and machine learning techniques to make clinical predictions and diagnostics in rehabilitation. Previously, he also held a five year biomechanist position with the Australian Institute of Sport tasked with improving the performance of Australian national athletes in preparation for the Olympics and World championships. In this role, he led research projects that include venue instrumentation, musculoskeletal testing, windtunnel testing and person-equipment interface design. Earlier, he researched the use of IMU-based sensor networks for motion capture at the University of Sydney with Professor Richard Smith.
My principal research interests lie in developing solutions that enable in-natura markerless motion capture for biomechanical modelling in Biomedical and Sports Engineering. Specifically, I am interested in the reconstruction of person-specific human pose, kinematics, and surface geometry to enhance our understanding of the non-linear behaviour of human motion, musculoskeletal injury and disease and enable modelling of soft-tissue dynamics. I specialise in translational applied research to develop innovative, highly accurate and tailored deployable evidence-based decision support tools for optimising sporting performance, diagnosis and treatment, improved neuromotor disorder identification using mobility degeneration classification models, and unimpeded patient monitoring of postural control, ambulatory activities and assisted living.
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