Myoelectricity signal identification method based on complexity, fractal dimension and fractal length

A technology of myoelectric signal and recognition method, applied in the field of pattern recognition, can solve the problems of unsatisfactory practicability of multi-sports-mode myoelectric control research, and the need to further improve the accuracy and real-time performance of multi-sports pattern recognition

Active Publication Date: 2014-03-05
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the practicability of research on multi-sport EMG control is not ideal, and the key iss

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  • Myoelectricity signal identification method based on complexity, fractal dimension and fractal length
  • Myoelectricity signal identification method based on complexity, fractal dimension and fractal length
  • Myoelectricity signal identification method based on complexity, fractal dimension and fractal length

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Embodiment Construction

[0049] The embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings: this embodiment is implemented on the premise of the technical solution of the present invention, and provides detailed implementation methods and specific operating procedures.

[0050] like figure 1 As shown, this embodiment includes the following steps:

[0051] Step 1: Obtain the sample data of the human upper limb EMG signal, specifically: firstly pick up the human upper limb EMG signal through the EMG signal acquisition instrument, and then use the signal denoising method based on wavelet energy spectrum entropy to denoise the EMG signal containing interference noise noise cancellation.

[0052] (1) Collect the EMG signals of the upper limbs of the human body. The subjects performed 50 groups of clenching, stretching, wrist internal rotation and wrist external rotation respectively. The extensor carpi ulnaris and flexor carpi ulnaris of the u...

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Abstract

The invention provides a myoelectricity signal identification method based on complexity, a fractal dimension and a fractal length, and aims to realize that a main operator can synchronously control a remote manipulator in a teleoperation robot system. A mode recognition characteristic uses an L-Z complexity index and fractal dimension index of a myoelectricity signal; a classifier uses an improved KNN model method which uses a clustering method as a data processing method; and an algorithm is equipped with an incremental learning capability. A hand movement speed of the operator depends on an activity intensity of arm muscles; and muscle activity intensity is represented by a maximum fractal length of the myoelectricity signal. In a certain range, the maximum fractal length of the myoelectricity signal and the hand movement speed of the operator form a monotone increasing function. The maximum fractal length of the myoelectricity signal serves as the input control amount, so as to realize the grasping speed control of the manipulator, and the good result is achieved.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of electromyographic signals, in particular to a method for recognizing movement speed of upper limbs and multi-movement patterns based on electromyographic signals, which is applied to control a teleoperated robot. Background technique [0002] A teleoperated robot is an operator-robot symbiotic interactive system, whose function is to realize the operator's teleoperation and teleperception of the remote environment. Among them, teleoperation is the remote operation of the operator on the remote robot, and the human command is transmitted to the robot. The teleoperation requires the operator's instruction to be transmitted to the robot through an input interface. At present, there are many remote operation input interfaces, but most of them still have certain problems, such as unnatural input, single method, and ambiguity in information. Therefo...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06K9/66
Inventor 张启忠朱海港左静高云园罗志增席旭刚
Owner HANGZHOU DIANZI UNIV
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