Multi-degree prosthetic hand control using a new BP neural network

  • Authors:

  • R. C. Wang;F. Li;M. Wu;J. Z. Wang;L. Jiang;H. Liu

  • Affiliations:

  • Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing, China;Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing, China;Northwestern University;Division of Intelligent and Biomechanical System, State Key Laboratory of Tribology, Tsinghua University, Beijing, China;Robotics Research Institute, Harbin Institute of Technology, Harbin, China;Robotics Research Institute, Harbin Institute of Technology, Harbin, China

  • Venue:
  • ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
  • Year:
  • 2006

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Abstract

A human-like multi-fingered prosthetic hand, HIT hand, has been developed in Harbin Institute of Technology. This paper presents a new pattern discrimination method for HIT hand control. The method uses a bagged-BP neural network based on combing the BP neural networks using bagging algorithm. Bagging has been used to overcome the problem of limited number of training data in uni-model systems, by combining neural networks as weak learners. We compared the results of the bagging based BP network, using four features, with the results obtained separately from these uni-feature systems. The results show that the bagged-BP network improves both the accuracy and stability of the BP classifier.