Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks
Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks
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DOI：
10.3390/s90302187
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发表时间：
2009-03-01
期刊：
影响因子：
3.9
通讯作者：
Sakoda, Saburo
中科院分区：
文献类型：
--
作者：
Shima, Keisuke;Tsuji, Toshio;Sakoda, Saburo
提出了一种利用对数线性化高斯混合网络(LLGMN)定量测量和评价手指敲击动作的方法，用于运动功能的评估。首先，利用磁性传感器测量手指的敲击动作，并计算出11个评价指标。这些指标在正常受试者的基础上标准化后，输入LLGMN进行运动功能评估。然后，结合基于套袋和熵的多个LLGMN的输出，对运动能力进行概率判别，以确定其是否正常。本文报道了33例帕金森病(PD)患者和32例正常老年人对手指敲击动作的评价和辨别实验。结果表明，使用12个LLGMN可以正确地对患者的损伤状况进行分类，准确率很高(平均准确率：93.1+/-3.69%)，比使用单个LLGMN的结果高出约5%。
This paper proposes a method to quantitatively measure and evaluate finger tapping movements for the assessment of motor function using log-linearized Gaussian mixture networks (LLGMNs). First, finger tapping movements are measured using magnetic sensors, and eleven indices are computed for evaluation. After standardizing these indices based on those of normal subjects, they are input to LLGMNs to assess motor function. Then, motor ability is probabilistically discriminated to determine whether it is normal or not using a classifier combined with the output of multiple LLGMNs based on bagging and entropy. This paper reports on evaluation and discrimination experiments performed on finger tapping movements in 33 Parkinson's disease (PD) patients and 32 normal elderly subjects. The results showed that the patients could be classified correctly in terms of their impairment status with a high degree of accuracy (average rate: 93.1 +/- 3.69 %) using 12 LLGMNs, which was about 5% higher than the results obtained using a single LLGMN.