A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects

Amit Salomon, Eran Gazit, Pieter Ginis, Baurzhan Urazalinov, Hirokazu Takoi, Taiki Yamaguchi, Shuhei Goda, David Lander, Julien Lacombe, Aditya Kumar Sinha, Alice Nieuwboer, Leslie C. Kirsch, Ryan Holbrook, Brad Manor and Jeffrey M. Hausdorff ()
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Amit Salomon: Tel Aviv Medical Center
Eran Gazit: Tel Aviv Medical Center
Pieter Ginis: Neuromotor Rehabilitation Research Group (eNRGy)
Alice Nieuwboer: Neuromotor Rehabilitation Research Group (eNRGy)
Leslie C. Kirsch: Michael J. Fox Foundation for Parkinson’s Research
Ryan Holbrook: Kaggle
Brad Manor: Hinda and Arthur Marcus Institute for Aging Research at Hebrew SeniorLife
Jeffrey M. Hausdorff: Tel Aviv Medical Center

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson’s disease. During a FOG episode, patients report that their feet are suddenly and inexplicably “glued” to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

Date: 2024
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DOI: 10.1038/s41467-024-49027-0

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