Abstract
This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.
| Original language | English |
|---|---|
| Pages (from-to) | 32-38 |
| Number of pages | 7 |
| Journal | Journal of Medical Engineering and Technology |
| Volume | 28 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2004 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
- Health Information Management
Cite this
Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic. / Chen, Yu Luen; Chen, Shih Ching; Chen, Weoi Luen; Hsiao, Chin Chih; Kuo, Te Son; Lai, Jin Shin.
In: Journal of Medical Engineering and Technology, Vol. 28, No. 1, 01.2004, p. 32-38.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic
AU - Chen, Yu Luen
AU - Chen, Shih Ching
AU - Chen, Weoi Luen
AU - Hsiao, Chin Chih
AU - Kuo, Te Son
AU - Lai, Jin Shin
PY - 2004/1
Y1 - 2004/1
N2 - This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.
AB - This study is aimed at establishing a neural network and fuzzy feedback control FES system used for adjusting the optimum electrical stimulating current to control the motion of an ankle joint. The proposed method further improves the drop-foot problem existing in hemiplegia patients. The proposed system includes both hardware and software. The hardware system determines the patient's ankle joint angle using a position sensor located in the patient's affected side. This sensor stimulates the tibialis anterior with an electrical stimulator that induces the dorsiflexion action and achieves the ideal ankle joint trace motion. The software system estimates the stimulating current using a neural network. The fuzzy controller solves the nonlinear problem by compensating the motion trace errors between the neural network control and actual system. The control qualities of various controllers for four subjects were compared in the clinical test. It was found that both the root mean square error and the mean error were minimal when using the neural network and fuzzy controller. The drop-foot problem in hemiplegic's locomotion was effectively improved by incorporating the neural network and fuzzy controller with the functional electrical simulator.
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UR - http://www.scopus.com/inward/citedby.url?scp=0842330276&partnerID=8YFLogxK
U2 - 10.1080/03091900310001211523
DO - 10.1080/03091900310001211523
M3 - Article
C2 - 14660183
AN - SCOPUS:0842330276
VL - 28
SP - 32
EP - 38
JO - Journal of Medical Engineering and Technology
JF - Journal of Medical Engineering and Technology
SN - 0309-1902
IS - 1
ER -