Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks
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 evaluati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2009-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/9/3/2187/ |
| _version_ | 1798041715407323136 |
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| author | Masaru Yokoe Akihiko Kandori Keisuke Shima Toshio Tsuji Saburo Sakoda |
| author_facet | Masaru Yokoe Akihiko Kandori Keisuke Shima Toshio Tsuji Saburo Sakoda |
| author_sort | Masaru Yokoe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T22:25:24Z |
| format | Article |
| id | doaj.art-406e7d7e1c7b41f1981cde0de8ec761f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:25:24Z |
| publishDate | 2009-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-406e7d7e1c7b41f1981cde0de8ec761f2022-12-22T03:59:45ZengMDPI AGSensors1424-82202009-03-01932187220110.3390/s90302187Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture NetworksMasaru YokoeAkihiko KandoriKeisuke ShimaToshio TsujiSaburo SakodaThis 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.http://www.mdpi.com/1424-8220/9/3/2187/Finger tapping movementsmagnetic sensorsneural networkspattern discriminationdiagnosis support |
| spellingShingle | Masaru Yokoe Akihiko Kandori Keisuke Shima Toshio Tsuji Saburo Sakoda Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks Sensors Finger tapping movements magnetic sensors neural networks pattern discrimination diagnosis support |
| title | Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks |
| title_full | Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks |
| title_fullStr | Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks |
| title_full_unstemmed | Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks |
| title_short | Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks |
| title_sort | measurement and evaluation of finger tapping movements using log linearized gaussian mixture networks |
| topic | Finger tapping movements magnetic sensors neural networks pattern discrimination diagnosis support |
| url | http://www.mdpi.com/1424-8220/9/3/2187/ |
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