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Analysis of the Severity of Dyskinesia in Patients with Parkinson's Disease via Wearable Sensors

机译:可穿戴传感器分析帕金森病患者患者的严重程度

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The aim of this study is to identify movement characteristics associated with motor fluctuations in patients with Parkinson's disease by relying on wearable sensors. Improved methods of assessing longitudinal changes in Parkinson's disease would enable optimization of treatment and maximization of patient function. We used eight accelerometers on the upper and lower limbs to monitor patients while they performed a set of standardized motor tasks. A video of the subjects was used by an expert to assign clinical scores. We focused on a motor complication referred to as dyskinesia, which is observed in association with medication intake. The sensor data were processed to extract a feature set responsive to the motor fluctuations. To assess the ability of accelerometers to capture the motor fluctuation patterns, the feature space was visualized using PCA and Sammon's mapping. Clustering analysis revealed the existence of intermediate clusters that were observed when changes occurred in the severity of dyskinesia. We present quantitative evidence that these intermediate clusters are the result of the high sensitivity of the proposed technique to changes in the severity of dyskinesia observed during motor fluctuation cycles.
机译:本研究的目的是通过依靠可穿戴传感器来识别与帕金森病患者的电机波动相关的运动特性。评估帕金森病的纵向变化的改进方法将实现患者功能的治疗和最大化。我们在上下肢上使用八个加速度计,以监控患者,同时执行一套标准化的电机任务。专家使用受试者的视频来指定临床评分。我们专注于称为止吐剂的电机并发症,与药物摄入相关联。处理传感器数据以提取响应于电机波动的特征设定。为了评估加速度计捕获电机波动模式的能力,可以使用PCA和Sammon的映射来可视化特征空间。聚类分析揭示了在动态血症严重程度发生的变化时观察到的中间簇。我们呈现定量证据,即这些中间簇是所提出的技术对多脉搏循环期间观察到的止吐瘤严重程度的变化的高灵敏度的结果。

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