Samà A, Pérez-Lopez C, Romagosa J, Rodríguez-Martín D, Català A, Cabestany J, Pérez-Martínez D A, Rodríguez-Molinero A
Technical Research Centre for Dependency Care and Autonomous Living (CETpD), Technical University of Catalonia (UPC), Vilanova i la Geltrú, Spain.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1194-7. doi: 10.1109/EMBC.2012.6346150.
Parkinson's Disease (PD) is a neurodegenerative disease that alters the patients' motor performance. Patients suffer many motor symptoms: bradykinesia, dyskinesia and freezing of gait, among others. Furthermore, patients alternate between periods in which they are able to move smoothly for some hours (ON state), and periods with motor complications (OFF state). An accurate report of PD motor states and symptoms will enable doctors to personalize medication intake and, therefore, improve response to treatment. Additionally, real-time reporting could allow an automatic management of PD by means of an automatic control of drug-administration pump doses. Such a system must be able to provide accurate information without disturbing the patients' daily life activities. This paper presents the results of the MoMoPa study classifying motor states and dyskinesia from 20 PD patients by using a belt-worn single tri-axial accelerometer. The algorithms obtained will be validated in a further study with 15 PD patients and will be enhanced in the REMPARK project.
帕金森病（PD）是一种会改变患者运动表现的神经退行性疾病。患者会出现许多运动症状：运动迟缓、运动障碍和步态冻结等。此外，患者在能够平稳运动数小时的时期（开期）和出现运动并发症的时期（关期）之间交替。准确报告帕金森病的运动状态和症状将使医生能够个性化用药，从而提高治疗反应。此外，实时报告可以通过自动控制给药泵剂量实现帕金森病的自动管理。这样的系统必须能够在不干扰患者日常生活活动的情况下提供准确信息。本文介绍了MoMoPa研究的结果——通过使用佩戴在腰部的单三轴加速度计对20名帕金森病患者的运动状态和运动障碍进行分类。所获得的算法将在另外15名帕金森病患者的研究中进行验证，并将在REMPARK项目中得到改进。
