2012 Fiscal Year Final Research Report
Gait Cycle Detection System Based On Learning For FES
| Project/Area Number |
22560257
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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 |
Intelligent mechanics/Mechanical systems
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| Research Institution | Akita National College of Technology |
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Principal Investigator |
KIZAWA Satoru 秋田工業高等専門学校, 機械工学科, 准教授 (90234202)
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| Project Period (FY) |
2010 – 2012
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| Keywords | バイオメカニクス |
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| Research Abstract |
After stroke or traumatic brain injury, hemiplegic patients often suffer from drop foot. Recently, an approach to management of drop foot is a functional electrical stimulation (FES) system, which can maintain the foot in a naturel position to prevent it from dragging during the swing phase of gait. However, it is necessary for FES system to detect the timing of the swing phase in order to control the electrical stimulation. So far, a heel sensor have been used to detect whether it is a timing of swing phase, but the heel sensor have problem of durability and discomfort during gait, therefore, we have studied another approach to detect the swing phase by using a tri-axial accelerometer, a gyroscope and the use of Neural Network Learning. As a result, although errors and delay times were slightly observed in the output of the sensor signals of the gait cycle detection system, non-handicapped persons who asked to walk by the developed system with FES could obtain a better walking ability.
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