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.
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References
Culmer, P.R., Jackson, A.E., Makower, S.: A control strategy for upper limb robotic rehabilitation with a dual robot system. IEEE/ASME Transactions on Mechatronics 15, 575–585 (2010)
Xu, B.G., Peng, S., Song, A.G.: Robot-aided upper-limb rehabilitation based on motor imagery EEG. Int. J. Adv. Robotic Sy. 8, 88–97 (2011)
Bovolenta, F., Sale, P.: Robot-aided therapy for upper limbs in patients with stroke-related lesions: Brief report of a clinical experience. Journal of Neuro Engineering and Rehabilitation 8 (2011)
Laura, M.C., David, J.R.: Review of control strategies for robotic movement training after neurologic injury. Journal of NeuroEngineering and Rehabilitation 6 (2009)
Choi, Y., Gordon, J., Kim, D.: An adaptive automated robotic task-practice system for rehabilitation of arm functions after stroke. IEEE Transactions on Robotics 25, 556–568 (2009)
Wolbrecht, E.T., Chan, V., Reinkensmeyer, D.J., Bobrow, J.E.: Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 16, 286–297 (2008)
Podobnik, J., Novak, D., Munih, M.: Grasp coordination in virtual environments for robot-aided upper extremity rehabilitation. Biomedical Engineering-Applications Basis Communications 23, 457–466 (2011)
Novak, D., Mihelj, M., Ziherk, J.: Psychophysiological measurements in a biocooperative feedback loop for upper extremity rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19, 400–410 (2011)
Gopura, R.A.R.C., Kiguchi, K.: An exoskeleton robot for human forearm and wrist motion assist-hardware design and EMG-based controller. Journal of Advanced Mechanical Design Systems and Manufacturing 2, 1067–1083 (2008)
Nuryani, N., Ling, S.S.H., Nguyen, H.T.: Electrocardiographic signals and swarm-based support vector machine for hypoglycemia detection. Annals of Biomedical Engineering 40, 934–945 (2012)
Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011)
Kim, K.J., Ahn, H.: A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research 39, 1800–1811 (2012)
Wu, S.Q., Er, M.J., Gao, Y.: A fast approach for automatic generation of fuzzy rulers by generalized dynamic fuzzy neural networks. IEEE Trans. Fuzzy Systems 9, 578–594 (2001)
Lim, W.K., Er, M.J.: Classification of mammographic masses using generalized dynamic fuzzy neural networks. Medical Physics 31, 1288–1295 (2004)
Ju, M.S., Lin, C.C.K., Lin, D.H., Hwang, I.S.: A rehabilitation robot with force-position hybrid fuzzy controller: Hybrid fuzzy control of rehabilitation robot. IEEE Transactions on Neural Systems and Rehabilitation Engineering 13, 349–358 (2005)
Sina, Z.M., Alireza, A., Javad, A.: A fast expert system for electrocardiogram arrhythmia detection. Expert Systems 27, 180–200 (2010)
Rosso, O.A., Martin, M.T., et al.: EEG analysis using wavelet-based information tools. Journal of Neuroscience Methods 153, 163–182 (2006)
Xu, G.Z., Song, A.G., Li, H.J.: Control system design for an upper-limb rehabilitation robot. Advanced Robotics 25, 229–251 (2011)
Song, A.G., Wu, J., Qin, G., et al.: A novel self-decoupled four degree-of-freedom wrist force/torque sensor. Measurement 40, 883–891 (2007)
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© 2012 Springer-Verlag Berlin Heidelberg
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Pan, L., Song, A., Xu, G., Li, H., Xu, B. (2012). Intelligent Prescription-Diagnosis Function for Rehabilitation Training Robot System. In: Su, CY., Rakheja, S., Liu, H. (eds) Intelligent Robotics and Applications. ICIRA 2012. Lecture Notes in Computer Science(), vol 7507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33515-0_2
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DOI: https://doi.org/10.1007/978-3-642-33515-0_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33514-3
Online ISBN: 978-3-642-33515-0
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