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Robot Control Method Based on Bayesian Classifier

A Bayesian classifier and control method technology, applied in the direction of program control manipulators, instruments, manipulators, etc., to achieve the effect of reducing randomness, high accuracy and strong adaptability

Active Publication Date: 2022-08-05
TIANJIN UNIV
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Problems solved by technology

[0007] Although the myoelectric control method based on pattern recognition has been greatly improved in terms of recognition accuracy and calculation speed, there is still room for improvement.

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  • Robot Control Method Based on Bayesian Classifier
  • Robot Control Method Based on Bayesian Classifier
  • Robot Control Method Based on Bayesian Classifier

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Embodiment Construction

[0054] The robot control method based on the Bayesian classifier of the present invention will be described in detail below with reference to the embodiments and the accompanying drawings.

[0055] like figure 1 As shown, the Bayesian classifier-based robot control method of the present invention includes the following steps:

[0056] 1) The Arduino muscle sensor is used to collect the surface EMG signals generated by the arm movement respectively, and the collected surface EMG signals generated by the arm movement are preprocessed by amplifying and filtering;

[0057] The surface EMG signals generated by the arm movement include the biceps brachii surface EMG signal and the triceps brachii surface EMG signal of the arm flexion movement, and the biceps brachii surface EMG signal and the brachial muscle surface EMG signal of the arm extension movement. Surface EMG signal of triceps.

[0058] 2) Divide the surface EMG signals generated by the preprocessed arm motion into a tra...

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Abstract

A robot control method based on Bayesian classifier: collecting surface EMG signals generated by arm motion respectively, and performing amplification and filtering preprocessing on the collected surface EMG signals generated by arm motion; The surface EMG signal generated by exercise is divided into training set and test set; the characteristics of the surface EMG signal of the training set and the test set are obtained respectively; according to the surface EMG signal characteristics of the training set, the characteristics of the surface EMG signal of the training set are obtained. Prior Probabilities; Motion Pattern Classification for Arm Movements Using a Bayesian Classifier. According to the characteristics of the surface electromyography signal, the invention designs a surface electromyography signal acquisition scheme, obtains the surface electromyography signal amplified by 240 times, and reduces the randomness of the surface electromyography signal. On the basis of analyzing the corresponding relationship between upper limb movements and related muscles, the biceps brachii and triceps brachii of the right upper limb were determined as the collection positions of the two-channel surface EMG signals, and the surface EMG signals with good consistency were obtained.

Description

technical field [0001] The invention relates to a robot control method. In particular, it relates to a Bayesian classifier-based robot control method. Background technique [0002] The advantages and disadvantages of EMG control methods mainly depend on the two evaluation indicators of recognition accuracy and calculation speed. [0003] Among the related technologies, the more representative technologies are the EMG control method based on threshold decision and the EMG control method based on pattern recognition. [0004] The basic principle of the EMG control method based on threshold decision-making is that after the EMG signal is corrected, filtered and modulated, a signal peak is correspondingly generated for a muscle contraction activity. stretch action. This method is relatively simple and runs relatively fast, but when the number of degrees of freedom is more than three, the application of this method is limited, and accurate identification cannot be performed. ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16G06K9/62
CPCB25J9/1602G06F18/24155
Inventor 侯庆志陈义丰高洁刘志强徐天一王建荣喻梅高深
Owner TIANJIN UNIV
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