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Fuzzy System Design of Myoelectric Prosthetic Hand Using PVDF Sensor and Touch Feedback

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This paper presents a method to classify surface myoelectric(SMES) signals which are utilized as control signals for a patient-responsive prosthetic hand system for paraplegics. This technique is based on two-channel and short period's real recordings from normal subjects' forearm muscle. Patterns of MES signals for different hand movements are classified via Wavelet Transform and a modified Least Squares Support Vector Machine (LS-SVM). Least Squares Support Vector Machine algorithm is utilized to implement the motion pattern recognition of surface MES. Machine learning can be effectively used to control on-line of a highly dexterous robotic hand in real time. A fuzzy controller is designed specially to control the adjustment of myoelectric prosthetic hand's movement, which can make the system of myoelectric prosthetic hand grasp object stably with a high degree of accuracy. The experimental results indicate that above method has high discrimination rate and good robust and represent a remarkable improvement with dexterous mechanical hands. We will be able to get a great potential in the area of bionic man–machine system such as control powered hand prostheses in a much finer way using SMES signal.


Document Type: Research Article


Publication date: 2011-10-01

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