Our society exhibits a worldwide trait of a quickly growing cohort of patients with neurodegenerative diseases, such as Parkinson's disease (PD). According to the analysts, there is a plausible “PD pandemic” to occur within the next two decades. Nowadays, the research in the area focuses on how to detect, predict, or classify PD and similar diseases without addressing the point of what activities or exercises a subject should do to improve the performance of these tasks. In this article, we propose a method based on machine learning (ML) and wearable sensors to identify the optimal exercises for the efficient detection of PD in patients. We first define 15 common tasks that are typically used to diagnose PD in modern clinical practice. However, these exercises still carry a high risk of misdiagnosis and, moreover, not all of them work well in the scope of existing ML solutions to support the diagnosis. Herein, we collect the data in a real clinical setting using a compact wearable wireless sensor node entailing a board gyroscope, accelerometer, and magnetometer. Application of ML methods to the collected data reveals three “most efficient” exercises to assist diagnosticians with the highest discriminating power (0.9 ROC AUC in each task). The proposed solution can be implemented as a medical decision support system for real-time PD diagnostics.

中文翻译：

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使用机器学习和可穿戴传感器定义有效检测帕金森病的最佳运动


我们的社会呈现出一个全球性的特征，即患有神经退行性疾病（例如帕金森病）的患者群体迅速增长。分析人士称，未来二十年内可能会出现“帕金森病大流行”。如今，该领域的研究重点是如何检测、预测或分类帕金森病和类似疾病，而不解决受试者应该进行哪些活动或锻炼来提高这些任务的表现。在本文中，我们提出了一种基于机器学习 (ML) 和可穿戴传感器的方法，用于识别有效检测患者 PD 的最佳运动。我们首先定义了现代临床实践中通常用于诊断 PD 的 15 项常见任务。然而，这些练习仍然存在很高的误诊风险，而且并非所有练习都能在现有机器学习解决方案范围内很好地支持诊断。在这里，我们使用包含板陀螺仪、加速度计和磁力计的紧凑型可穿戴无线传感器节点在真实的临床环境中收集数据。将 ML 方法应用于收集的数据揭示了三种“最有效”的练习，以最高的辨别能力（每个任务中的 ROC AUC）协助诊断人员。所提出的解决方案可以作为实时局部放电诊断的医疗决策支持系统来实施。