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.

冻结步态 (FOG) 是一种使人衰弱的问题，严重损害 38-65% 的帕金森病患者的活动能力和独立性。在 FOG 发作期间，患者报告说他们的脚突然莫名其妙地“粘”在地板上。缺乏广泛适用、客观的光纤陀螺检测方法阻碍了研究和治疗。为了解决这个问题，我们组织了为期3个月的机器学习竞赛，邀请来自世界各地的专家开发基于可穿戴传感器的FOG检测算法。来自 83 个国家的 1,379 个团队提交了 24,862 个解决方案。获奖解决方案在 FOG 检测中表现出高精度、高特异性和良好的精度，与黄金标准参考具有很强的相关性。当应用于连续 24/7 数据时，这些解决方案揭示了日常生活中 FOG 发生中以前未观察到的模式。这一成功的努力凸显了机器学习竞赛在快速吸引人工智能专家解决关键医疗挑战方面的潜力，并为客观的 FOG 量化提供了一种有前景的方法。