Chandrabhatla Anirudha S, Pomeraniec I Jonathan, Ksendzovsky Alexander
School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA.
NPJ Digit Med. 2022 Mar 18;5(1):32. doi: 10.1038/s41746-022-00568-y.
Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
帕金森病（PD）是一种神经退行性疾病，其特征在于运动障碍，如震颤、运动迟缓、运动障碍和步态异常。目前的方案在门诊就诊期间评估帕金森病症状，可能具有主观性。患者日记有助于临床医生评估居家症状，但可能不完整或不准确。因此，研究人员开发了居家自动化方法来监测帕金森病症状，以实现数据驱动的帕金森病诊断和管理。我们查询了美国国立医学图书馆的PubMed数据库，以分析用于监测帕金森病常见运动症状的技术以及计算/机器学习方法的进展。我们审查了大约12000篇论文的子集，这些论文最能描述从文献综述中得出的机器学习和技术时间表。在过去的五十年里，用于监测帕金森病运动症状的技术有了显著进步。早期监测始于实验室设备，如针电极肌电图，随后过渡到实验室加速度计/陀螺仪，然后是可穿戴加速度计/陀螺仪，最后是基于手机以及移动和网络应用程序的居家监测。在使用机器学习算法对帕金森病患者进行分类方面也取得了重大进展。研究人员利用来自不同设备（如摄像机、基于手机的加速度计）的数据，设计了基于神经网络和非神经网络的机器学习算法，以对帕金森病患者的震颤、步态、运动迟缓及运动障碍进行分类。用于监测帕金森病运动症状的技术和计算技术在过去五十年中的共同发展推动了显著进展，使帕金森病症状的监测从实验室/诊所转向居家监测。
