A hand gesture recognition method for myoelectric prosthesis based on community voting mechanism
A voting mechanism and gesture recognition technology, applied in the fields of artificial intelligence and rehabilitation medicine, can solve problems such as high hardware and software requirements, high-end sensors are expensive, and inconvenient to wear, so as to improve living conditions, promote the development of the cause of the disabled, and identify fast effect
Active Publication Date: 2019-03-26
SHANGHAI NORMAL UNIVERSITY
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These two identification methods are based on a macro perspective. The former has high requirements on software and hardware, such as the acquisition effect of the camera, the technical functions of the image processing software, and even the ambient light intensity of the outside world when taking pictures; the latter requires wearing a large number of sensors, high-end Sensors are expensive and inconvenient to wear
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[0047] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.
[0048] A hand gesture recognition method for myoelectric prosthetics based on a community voting mechanism, such as figure 1 and figure 2 shown, including:
[0049] Step S1: Use the surface electromyography sensor to collect different gesture action signals, and extract the feature value of each action after preprocessing;
[0050] Step S2: Establish a BP neural network, the input of the BP neural network is the eigenvalue of the gesture action, and the output is the gesture action corresponding to the eigenvalue;
[0051] Step S3: Use the square of the error between the output of...
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The invention relates to a hand gesture recognition method of myoelectric prosthesis based on community voting mechanism, comprising the following steps: S1, collecting different hand gesture action signals by using a surface myoelectric signal sensor, and extracting eigenvalues of each action after preprocessing; 2, establishing a BP neural network, wherein that input of the BP neural network isthe eigenvalue of the gesture action, and the output is the gesture action corresponding to the eigenvalue; S3, taking the error square between the output of the BP neural network and the actual output as a fitness function, and adopting an algorithm based on a community voting mechanism to optimize the neural network parameters; Step S4: Bring the test data set into the BP neural network for gesture recognition. Compared with the prior art, the present invention has the advantages of high identification accuracy.
Description
technical field [0001] The invention relates to the fields of artificial intelligence and rehabilitation medicine, in particular to a method for recognizing hand gestures of myoelectric prosthesis based on a community voting mechanism. Background technique [0002] Rehabilitation robots are closely related to human health and are one of the development directions of intelligent robots, involving rehabilitation medicine, sports bionics, ergonomics and other fields. The prosthetic hand is a kind of intelligent robot, and its key technologies are biological human-machine interface technology and intelligent control technology. In my country, the number of people with physical disabilities caused by traffic accidents, earthquake disasters, industrial injuries, etc., and central nervous system damage and motor dysfunction caused by stroke, Parkinson's disease, and spinal cord injury is increasing by more than one million every year, and the aging population is intensifying. In ad...
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IPC IPC(8): G06K9/00G06N3/00G06N3/04A61F2/72
CPCG06N3/006A61F2/72G06V40/28G06N3/044G06F2218/02G06F2218/12
Inventor 陈佳佳茅红伟张倩陈广钦
Owner SHANGHAI NORMAL UNIVERSITY