Barth Jens, Klucken Jochen, Kugler Patrick, Kammerer Thomas, Steidl Ralph, Winkler Jürgen, Hornegger Joachim, Eskofier Björn
Department of Molecular Neurology, University Hospital of Erlangen, Erlangen, Germany.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:868-71. doi: 10.1109/IEMBS.2011.6090226.
Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists.
帕金森病（PD）是最常见的神经退行性运动障碍。早期诊断和有效的治疗监测是治疗患者和降低医疗成本的重要前提。为实现这一目标，迫切需要客观且非侵入性的评估策略。在本研究中，我们应用一种基于移动、轻便且易于应用的传感器的步态分析系统来测量帕金森病患者的步态模式，并区分轻度和重度步态损伤。纳入了16名健康对照者、14名早期帕金森病患者和13名中期帕金森病患者。受试者在穿着配备惯性传感器（陀螺仪和加速度计）的运动鞋时进行标准化步态测试。信号被无线记录，特征被提取，并使用不同的分类算法对不同亚组进行分类。所展示的系统能够以88%的灵敏度和86%的特异性对患者和对照者进行分类（用于早期诊断）。此外，它能够以100%的灵敏度和100%的特异性区分轻度和重度步态损伤（用于治疗监测）。该系统或许能够客观地对帕金森病步态模式进行分类，为患者、护理人员和治疗师提供重要且互补的信息。
