Hand motion recognition method based on surface electromyography signal decomposition

A technology of hand movements and recognition methods, applied in character and pattern recognition, computer components, instruments, etc., can solve the problems of sensor increase, noise interference, system complexity increase, etc., to reduce the number, improve accuracy, and improve stability sexual effect

Active Publication Date: 2018-01-02
SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The increase in the number of sensors, on the one hand, increases the complexity of the system, on the other hand, it also brings greater noise interference, and due to the limitations of muscle shape and sensor volume, fewer sensors are used to recognize more gestures, is an important issue that needs to be resolved

Method used

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  • Hand motion recognition method based on surface electromyography signal decomposition
  • Hand motion recognition method based on surface electromyography signal decomposition
  • Hand motion recognition method based on surface electromyography signal decomposition

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Embodiment Construction

[0081] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0082] Such as figure 1 Shown is the method flow chart of the present invention, gathers the sEMG signal y of pronator quadratus muscle place t , and perform second-order differential filtering, the method is as follows:

[0083] x t =y t+2 -y t+1 -y t +y t-1

[0084] where x t is the filtered sEMG signal, and the subscript t is the sampling time.

[0085] For the filtered sEMG signal x t , for peak detection, the calculation formula of the threshold α is as follows:

[0086]

[0087] Among them, c 1 is an empirical constant equal to 3.5, If the threshold is exceeded, a spike is considered to appear.

[0088] record x t The sampling point x that crosses the threshold α from bottom to top i , and adjacent sampling points x crossing the threshold from top to bottom i+k . The peak-to-peak value is x i to x i+k The maximum v...

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Abstract

The invention relates to a hand motion recognition method based on surface electromyography (sEMG) decomposition. The method includes two parts: decomposing sEMG signals to obtain a motor unit action potential sequence (MUAPT); and a hand motion recognition method based on MUAPT. The sEMG signal decomposition consists of sEMG signal preprocessing, sEMG peak detection and Gaussian mixture model (GMM) clustering; while the hand movement recognition based on MUAPT includes feature extraction, principal component analysis (PCA) dimensionality reduction, LDA classification, etc. Under the condition that only one channel sEMG is used, the invention uses the motor unit action potential information obtained by decomposing the sEMG signal to identify hand movements, effectively improves the single-channel sEMG recognition rate, and has important theoretical significance and practical application value.

Description

technical field [0001] The invention relates to the technical field of biological signal recognition, and specifically relates to a hand movement recognition method based on surface electromyography signal decomposition. Background technique [0002] Hand motion recognition has become one of the important methods of human-computer interaction, widely used in sign language recognition, prosthetic control, somatosensory game control, teleoperation and other fields. The hand movement recognition method based on electromyographic signal (sEMG) is real-time, convenient and non-invasive, and is more suitable for rehabilitation fields such as helping the elderly and the disabled, and has received more and more attention. [0003] Traditional hand motion recognition methods often use multiple channels of sEMG data [1-8] to extract the corresponding time domain [4,5], frequency domain [6,7], time-frequency domain [8] features, Use specific data classification algorithms [9-11] to co...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
Inventor 韩建达赵新刚熊安斌丁其川赵忆文
Owner SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
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