摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-关于人工智能的新研究结果已经发表。根据NewsRx记者从特拉维夫医学中心传来的消息，研究表明：“步态冻结(FOG)是一个令人衰弱的问题，严重损害了3-65%的帕金森病患者的行动能力和独立性。”这项研究的财政支持者包括迈克尔·J·福克斯基金会的帕尔·金森的研究。我们的新闻记者从特拉维夫医疗中心获得了这项研究的一句话：“在雾中，病人报告说，他们的脚突然莫名其妙地‘粘’在地板上。由于缺乏一种广泛适用的客观检测方法，阻碍了研究和治疗。为了解决这个问题，我们组织了一场为期三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雾探测算法。来自83个国家的1379个团队提交了24862个解决方案。获胜的解决方案在雾探测方面表现出高精度、高特异性和良好的精度，与黄金标准参考有很强的相关性。当应用于连续的24/7数据时，这些解决方案揭示了以前在日常生活中的雾发生事件中未观察到的模式。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from the Tel Aviv Medi cal Center by NewsRx correspondents, research stated, "Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 3 8-65% of people with Parkinson's disease." Financial supporters for this research include Michael J. Fox Foundation For Par kinson's Research. Our news correspondents obtained a quote from the research from Tel Aviv Medical Center: "During a FOG episode, patients report that their feet are suddenly and inexplicably ‘glued' to the floor. The lack of a widely applicable, objective F OG detection method obstructs research and treatment. To address this problem, w e organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams fr om 83 countries submitted 24,862 solutions. The winning solutions demonstrated h igh 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 occur rences."
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