Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients
Detecting freezing of gait with a tri-axial accelerometer in Parkinson's disease patients
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DOI：
10.1007/s11517-015-1395-3
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发表时间：
2016-01-01
影响因子：
3.2
通讯作者：
Rodriguez-Molinero, Alejandro
中科院分区：
文献类型：
--
作者：
Ahlrichs, Claas;Sama, Albert;Rodriguez-Molinero, Alejandro
步态冻结（FOG）是帕金森氏病（PD）的常见运动症状，它表现为无法启动或继续步态。本文介绍了一种仅根据从腰部装置获得的加速度测量值监视雾发作的方法。测试了该方法的三个近似值。最初，支持向量机（SVM）直接检测雾。然后，分类器的输出会随着时间的推移而汇总以确定置信值，该置信值用于冻结的最终分类（即第二和第三种方法）。所有变异均经过15例患者的信号训练，并通过另外5名患者的信号进行了评估。使用线性SVM内核，第三种方法提供了98.7％的精度和96.1％的几何方法。此外，研究频率特征是否足以可靠地检测雾。结果表明，这些功能允许该方法以超过90％的精度检测雾，并且该频率功能可以通过简单地使用腰围传感器来可靠地监测雾。
Freezing of gait (FOG) is a common motor symptom of Parkinson's disease (PD), which presents itself as an inability to initiate or continue gait. This paper presents a method to monitor FOG episodes based only on acceleration measurements obtained from a waist-worn device. Three approximations of this method are tested. Initially, FOG is directly detected by a support vector machine (SVM). Then, classifier's outputs are aggregated over time to determine a confidence value, which is used for the final classification of freezing (i.e., second and third approach). All variations are trained with signals of 15 patients and evaluated with signals from another 5 patients. Using a linear SVM kernel, the third approach provides 98.7 % accuracy and a geometric mean of 96.1 %. Moreover, it is investigated whether frequency features are enough to reliably detect FOG. Results show that these features allow the method to detect FOG with accuracies above 90 % and that frequency features enable a reliable monitoring of FOG by using simply a waist sensor.