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

Sensors
Keisuke ShimaS Sakoda

Abstract

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.

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Citations

Jun 3, 2010·The Review of Scientific Instruments·Akihiko KandoriSaburo Sakoda
Dec 18, 2013·Artificial Intelligence in Medicine·Taha KhanMark Dougherty
Dec 24, 2014·Sensors·Carlos A Perez-RamirezMario A Ibarra-Manzano
Apr 2, 2016·Medical & Biological Engineering & Computing·Yuko SanoSaburo Sakoda
Nov 28, 2015·Natural Computing·Bożena KostekAndrzej Czyżewski
Jul 15, 2015·Biomedical Engineering Online·Houde DaiTim C Lueth
Dec 7, 2016·Methods of Information in Medicine·Sofija SpasojevićJosé Santos-Victor
Jan 27, 2017·Sensors·Milica Djurić-JovičićAntonije R Djordjević
Feb 23, 2017·Journal of Parkinson's Disease·Hasan HasanAlastair J Noyce
Aug 30, 2017·Frontiers in Neurology·Sofija SpasojevićJosé Santos-Victor
Dec 11, 2020·Journal of Atherosclerosis and Thrombosis·Tomohisa NezuHirofumi Maruyama

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