Researchers at TAU's Faculty of Medical & Health Sciences invited the international community of machine learning researchers to participate in a contest devised to advance their study and assist neurologists: developing a machine learning model to support a wearable sensor for continuous, automated monitoring and quantification of freezing of gait (FOG) episodes in people with Parkinson's disease. Close to 25,000 solutions were submitted, and the best algorithms were incorporated into the novel technology. The study was led by Prof.
Jeff Hausdorff from the Department of Physical Therapy at the Faculty of Medical & Health Sciences and the Sagol School of Neuroscience at Tel Aviv University, and the Center for the Study of Movement, Cognition and Mobility at the Tel Aviv Medical Center, together with Amit Salomon and Eran Gazit from the Tel Aviv Medical Center. Other investigators included researchers from Belgium, France, and Harvard University. The paper was in and featured in the journal's Editors' Highlights.
Prof. Hausdorff, an expert in the fields of gait, aging, and Parkinson's disease, explains, "FOG is a debilitating and so far unexplained phenomenon, affecting 38–65% of Parkinson's sufferers. A FOG episode can last from a few seconds to more than a minute, during which the patient's feet are suddenly 'glued' to the floor, and the person is unable to begin or continue walking.
"FOG can seriously impair the mobility, independence, and quality of life of people with P.