Development of simple jaw-tracking system that applies pattern recognition technology
| Project/Area Number |
16591941
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
補綴理工系歯学
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| Research Institution | Osaka University |
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Principal Investigator |
MIZUMORI Takahiro Osaka University, Dental Hospital, Assistant professor, 歯学部附属病院, 講師 (10200023)
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| Co-Investigator(Kenkyū-buntansha) |
YATANI Hirofumi Osaka University, Graduate School of Dentistry, Professor, 大学院・歯学研究科, 教授 (80174530)
NAKAMURA Takashi Osaka University, Graduate School of Dentistry, Associate Professor, 大学院・歯学研究科, 助教授 (20198211)
ISHIGAKI Shoichi Osaka University, Graduate School of Dentistry, Assistant professor, 歯学部附属病院, 講師 (40212865)
SOHMURA Taiji Osaka University, Graduate School of Dentistry, Associate Professor, 大学院・歯学研究科, 助教授 (10154692)
KINUTA Soichiro Osaka University, Dental Hospital, Lecturer, 歯学部附属病院, 医員 (60397651)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2005: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2004: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | jaw movements / neural network / motion capture / masticatory movements / 顎運動計測システム |
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| Research Abstract |
We are attempted to establish a neural-network algorithm to presume incisal movement from peri-oral movements in order to develop a new jaw-tracking system without complicated devices around subject's mouth. A marker to detect jaw movements for control, with a circle of 10 mm diameter, was attached to the mandibular incisors to detect real jaw movements and eight same markers were attached around the subject's mouth to detect peri-oral movements to estimate jaw movements by neural network algorithm. Lateral border movement and maximum opening one time for neural network learning and four times of them and masiticatory movements for verification were recorded by a digital camcorder in front of the subject. In the movement for learning, neural network software (Neurosim/L, Fujitsu) learned the relation between the incisal movements and peri-oral movements. In the movements for verification, incisal movements were presumed from the peri-oral movements by using the learned neural network. Presumed incisal coordinates and measured incisal coordinates were compared and simple regression analyses were carried out. The y values of the incisal movements are presumed from peri-oral movements by utilizing neural network with high precision. In lateral movements, the x values of them are also presumed with high precision. However, in maxmum opening and gum chewing, the x values were not precise. This may be improved by modification of the learning methods of the neural network. It was suggeted that the incisal movement can be presumed from the movement of the peri oral tissue by the neural network.
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Report
(3 results)
Research Products
(9 results)
