| Project/Area Number |
63570368
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Neurology
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| Research Institution | National Center of Neurology and Psychiatry (NCNP) |
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Principal Investigator |
SHIBASAKI Hiroshi NINS, NCNP, Head of Division, 部長 (30037444)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAMURA Masatoshi Department of Electrical Engineering, Professor, 理工学部, 教授 (50038080)
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| Project Period (FY) |
1988 – 1989
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| Project Status |
Completed (Fiscal Year 1989)
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| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 1989: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1988: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Central motor control / Visual target tracking / Digitizer / Computer model / Cerebellar ataxia / Parkinson's disease / 基底核障害 |
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| Research Abstract |
In the first year, a system was devised for recording and processing hand movement in pursuing a visual target which moves in various ways and with variable speed on the two dimensional screen. By using a mouse for registering the hand movement in this system, 8 patients with cerebellar ataxia and 5 with Parkinson's disease were studied and compared with 20 healthy subjects (11 young and 9 aged). By analyzing the error between the visual target and the hand movement trace in time domain, feature parameters for each subject group were extracted, and automatic classification of each subject into one of the four groups was successfully implemented based on a nonlinear discrimination function using the extracted feature parameters.
During the second year, the mouse used in the above study was replaced by a digitizer in order to put the hand in a more natural position during the test. In fact, the subject pursued the moving visual target by holding and moving a stylus pen by hand on the digitizer, and the similar analysis was carried out in 9 patients with cerebellar ataxia and 6 with Parkinson's disease in comparison with 16 healthy subjects. Automatic discrimination between the patient groups and the normal control group was easily attained, but that between the two disease groups was not still satisfactory. Analysis of movement model of each subject group based on the feature parameters will further increase the efficiency of this system.
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