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

Medicine and Health

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

A. Salomon, E. Gazit, et al.

This groundbreaking study organized a machine-learning contest to tackle the challenging freezing of gait (FOG) in Parkinson's disease, attracting 1,379 teams and resulting in 24,862 solutions. The winning algorithms not only exhibited remarkable accuracy, but also unveiled new insights into FOG occurrences during daily life. Conducted by a diverse team of experts including Amit Salomon and Leslie C. Kirsch, this research showcases the transformative potential of machine learning in addressing critical medical issues.

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Playback language: English
Abstract
Freezing of gait (FOG) is a debilitating symptom in Parkinson's disease. This study organized a machine-learning contest to develop wearable sensor-based FOG detection algorithms. 1,379 teams submitted 24,862 solutions. Winning solutions demonstrated high accuracy and revealed previously unobserved patterns in daily living FOG occurrences, highlighting the potential of machine learning contests for addressing critical medical challenges.
Publisher
Nature Communications
Published On
Jun 06, 2024
Authors
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, Jeffrey M. Hausdorff
Tags
Freezing of gait
Parkinson's disease
machine learning
wearable sensors
healthcare
FOG detection
medical challenges

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