Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors.
Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors.
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DOI：
10.1109/titb.2009.2033471
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Bonato P
中科院分区：
文献类型：
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作者：
Patel S;Lorincz K;Hughes R;Huggins N;Growdon J;Standaert D;Akay M;Dy J;Welsh M;Bonato P
本文介绍了一项试点研究的结果，以评估使用加速度计数据来估计帕金森病患者的症状和运动并发症的严重程度的可行性。一个支持向量机（SVM）分类器的实施，以估计震颤，运动迟缓和运动障碍的严重程度，从加速度计的数据特征。将基于SVM的估计值与通过目视检查患者执行一系列标准化运动任务时拍摄的视频记录获得的临床评分进行比较。由接受过使用帕金森症状和运动并发症严重程度评估量表培训的临床医生对视频记录进行分析。分析了从加速度计时间序列导出的结果，以评估用于从加速度计数据导出片段（最终计算数据特征）的窗口持续时间对临床评分估计的影响，使用不同的支持向量机内核和误分类成本值，以及使用从不同运动任务导出的数据特征。还对结果进行了分析，以评估哪些数据特征组合携带了足够的信息来可靠地评估症状和运动并发症的严重程度。考虑到与估计身体传感器网络的节点上的每个数据特征相关联的计算成本以及使用这些数据特征对基于SVM的帕金森症状和运动并发症的严重程度的估计的可靠性的影响，对数据特征的组合进行了比较。
This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson’s disease. A Support Vector Machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different support vector machine kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.