Ramdhani Ritesh A, Khojandi Anahita, Shylo Oleg, Kopell Brian H
Department of Neurology, School of Medicine, New York University, New York City, NY, United States.
Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN, United States.
Front Comput Neurosci. 2018 Sep 11;12:72. doi: 10.3389/fncom.2018.00072. eCollection 2018.
The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease.
运动传感器作为一种可为帕金森病患者提供客观运动表现数据的工具，其出现为拓展这种神经退行性疾病的临床护理视野提供了契机。主观临床量表和基于患者的运动日记的临床测量属性有限，只能提供对运动障碍的粗略而非连续实时的观察。此外，机器学习算法的扩展正在产生新的分类和概率临床模型，这些模型有望改变现有的治疗模式，优化先进疗法的应用，并可能促进针对该疾病的疾病修饰药物的开发和测试。我们综述了惯性传感器和机器学习算法在帕金森病中的应用。
