Myoelectricity classification method based on improved small-world echo state network

A technology of echo state network and classification method, which is applied in the field of pattern recognition and can solve problems such as poor anti-interference

Active Publication Date: 2020-01-31
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, the time-domain feature extraction method itself deals with stationary signals, and the time-domain extraction is based on the signal amplitude, and its anti-interference is poor

Method used

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  • Myoelectricity classification method based on improved small-world echo state network
  • Myoelectricity classification method based on improved small-world echo state network
  • Myoelectricity classification method based on improved small-world echo state network

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Experimental program
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Embodiment Construction

[0035] Such as figure 1 As shown, this embodiment includes the following steps:

[0036] Step 1: Collect 4 channels of EMG signals from the human gastrocnemius, tibialis anterior, vastus medialis, and vastus externus when the human body is doing daily behaviors. The experimental actions include falling, walking, sitting, squatting, going upstairs, and going downstairs. The experiment selected healthy males as the experimental subjects, and the subjects were required to refrain from strenuous exercise one week before the experiment, so as to avoid the muscle shaking caused by muscle fatigue and affect the accuracy of the EMG signal. The experiment adopts Delsys all-wireless surface electromyography test system, Trigno TM wireless myoelectric sensor, figure 2 A four-channel EMG signal map of falls collected for an experimenter.

[0037] Step 2, initialize the input connection matrix W in , internal connection matrix W res and the output feedback matrix W back , W in , W ...

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Abstract

The invention discloses a myoelectricity classification method based on an improved small-world echo state network. Firstly, a small-world network is used for improving a reserve pool structure of theESN, then an edge adding probability is used for improving the small-world network, and the network is called as an improved small-world echo state network, so that the adaptability of a reserve poolis improved, and the generalization ability and stability of the ESN are improved. Then, the output weight of the network can be obtained by training the network, and the output weight is used as a corresponding feature. Electromyographic signals of six actions of falling down, walking, sitting, squatting, going upstairs and going downstairs are collected, corresponding features are extracted through ISWLESN, and then feature dimensions are reduced through PCV. And finally, the performance of the network features is represented by using the scatter diagram, the class separability index and the DBI. Results show that ISWLESN has good clustering performance, and has high precision when used for support vector machine classification.

Description

technical field [0001] The invention belongs to the field of pattern recognition and relates to a method for extracting myoelectric features based on an improved small-world echo state network. Background technique [0002] Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contraction, and has been widely used in clinical diagnosis and rehabilitation medicine because it can directly reflect neuromuscular activity. At the same time, multi-channel surface EMG signals can provide a safe and non-invasive control method for controlling the movement of prosthetic limbs and other advanced human-machine interfaces. In recent years, with the continuous development of detection technology, signal processing methods, and computing technology, extracting effective features from raw EMG signals has become one of the hot issues in surface EMG applications. [0003] The key to the analysis and processing of surface electromyography signal is the sele...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08A61B5/0488
CPCG06N3/08A61B5/7267A61B5/389G06N3/045G06F2218/08G06F2218/12G06F18/2135G06F18/2411
Inventor 姜文俊席旭刚刘晓云邱宇晗孙紫阳郝奇奇马存斌
Owner HANGZHOU DIANZI UNIV
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