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Finger activity recognition system based on arm myoelectricity network

A technology of activity recognition and electromyography, which is applied in the fields of biomedical information and brain-computer interface, and can solve problems such as electrode displacement.

Active Publication Date: 2021-06-11
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there are many inherent problems in the extraction and analysis of EMG signals, such as electrode displacement, sweating, fatigue, etc., and due to the mutual interference of adjacent muscles, only single-channel EMG signals can be used to detect the state of deep muscles without damage.

Method used

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  • Finger activity recognition system based on arm myoelectricity network
  • Finger activity recognition system based on arm myoelectricity network
  • Finger activity recognition system based on arm myoelectricity network

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Embodiment Construction

[0033] according to image 3 The technical solution of the present invention will be described in detail; the front end task prompt interface of the system is used to prompt the user to make a finger action; through the data acquisition module, the system is synchronously collects the user's electromyrower signal, collecting good myocardiogram directly stored directly to memory, another Aspects are input to the data calculation module; the data thread extracts the network feature in the electromymic signal for parameter training and finger task classification test; the final result output module stores the classification results and displayed.

[0034] The task prompt module is primarily used for training and finger activity tasks during the test, including training prompt interface and synchronous test prompt interface. Users can freely change the relevant training parameters of the task prompt interface to obtain better training and testing.

[0035] The task prompt interface in ...

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Abstract

The invention discloses a finger activity recognition and classification system based on an arm myoelectricity network, belongs to the technical field of biomedical information, and particularly relates to a mode classification method in the field of brain-computer interfaces. The system comprises two test modes, after simple training, a synchronous test mode is selected, and the system can recognize actions made by a user following a task prompt interface; or a real-time test mode is selected, and the system can identify the action made by the user in real time; and more application scenes can be met by integrating the two test modes. The system is high in recognition efficiency and easy to operate, and is expected to play a role in dyskinesia rehabilitation treatment and an online BCI system.

Description

Technical field [0001] The invention belongs to the field of biomedical information technology, and specifically, the present invention relates to the pattern classification method in the field of brain-machine interface. Background technique [0002] Electronic EMGRAPHY (EMG) is an important electrophysiological signal generated by muscle contraction, comprising a large number of neural information related to limb movement. The electromyography is simultaneously recorded by an electrophysiological activity from different muscle units simultaneously attached to the skin surface of the skin, which comprises information on the discharge sequence of driving the muscle motor neurons. [0003] The human muscle activity is much more complicated than what we see. A simple finger action also requires multiple muscles, and studies have shown that the motion system is superimposed through different groups of muscles, and multiple muscle activity is combined as simple actions. At present, t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/389A61B5/00
CPCA61B5/72A61B5/7203A61B5/7267
Inventor 徐鹏李宁张夏冰李存波汪义锋李发礼尧德中
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA