Intelligent prescription-diagnosis function for rehabilitation training robot system

Authors:
Lizheng Pan;Aiguo Song;Guozheng Xu;Huijun Li;Baoguo Xu
Affiliations:
School of Instrument Science and Engineering, Southeast University, Nanjing, China;School of Instrument Science and Engineering, Southeast University, Nanjing, China;College of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China;School of Instrument Science and Engineering, Southeast University, Nanjing, China;School of Instrument Science and Engineering, Southeast University, Nanjing, China
Venue:
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
Year:
2012

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Abstract

A prescription-diagnosis function based on integrating support vector machine and generalized dynamic fuzzy neural networks (SVM-GDFNN) is developed to automatically recommend a suitable training mode to the impaired limb. Considering the outstanding generalization ability and misclassified samples mainly distributed nearby the support vector for SVM method, SVM is adopted to recommend a preliminary prescription diagnosis for the sample and GDFNN is employed to rediagnose the sample nearby the support vector. Finally, the training mode of impaired limb is prescribed according to the designed principles. In addition, wavelet packet decomposition is applied to extract the features representing the impaired-limb movement performance. Clinical experiment results indicate that the suggested method can effectively reduce the misdiagnosis and serve with a high diagnostic accuracy. Meanwhile, the designed rehabilitation system well manages the promising prescription-diagnosis function, improving the intelligent level.