Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks

Sensors

Keisuke Shima²  Toshio Tsuji²  Akihiko Kandori³  Masaru Yokoe¹ 

[1] Graduate School of Medicine, Osaka University, 1-1 Yamadaoka, Suita, Osaka, Japan; E-Mails:;Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan; E-Mail:;Advanced Research Laboratory, Hitachi Ltd., 2520 Hatoyama, Saitama, Japan; E-Mail:

关键词: Finger tapping movements;    magnetic sensors;    neural networks;    pattern discrimination;    diagnosis support;   

DOI  :  10.3390/s90302187

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee MDPI, Basel, Switzerland