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Brain muscle function network motion fatigue detection method based on symbolic transfer entropy and graph theory

A functional network, exercise fatigue technology, applied in the field of pattern recognition, can solve problems such as increasing muscle injury and reducing the ability of muscles to generate force

Pending Publication Date: 2021-03-23
HANGZHOU DIANZI UNIV
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  • Application Information

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Problems solved by technology

The most intuitive effect is a reduction in the muscle's ability to generate force, which increases the likelihood of muscle injury

Method used

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  • Brain muscle function network motion fatigue detection method based on symbolic transfer entropy and graph theory
  • Brain muscle function network motion fatigue detection method based on symbolic transfer entropy and graph theory
  • Brain muscle function network motion fatigue detection method based on symbolic transfer entropy and graph theory

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Embodiment Construction

[0065] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings. This embodiment is carried out on the premise of the technical solution of the present invention, and a detailed implementation manner and specific operation process are given.

[0066] Implementation paradigm and experimental environment description:

[0067] E1 non-fatigue state paradigm: the subject sits on a chair naturally, the forearm is naturally placed on the table directly in front of the subject, and at the same time holds the gripper in his hand. Start according to the computer audio signal prompt, complete 30% of the maximum grip within 1s, then keep for 5s, and end according to the computer audio signal prompt. A 2-minute rest period was given between each experiment to avoid muscle fatigue.

[0068] E2 Fatigue State Paradigm: The subject sits on a chair naturally, the forearm is naturally placed on the table directly in front of the subje...

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Abstract

The invention discloses a brain muscle function network motion fatigue detection method based on symbol transfer entropy and a graph theory. The method comprises the following steps that firstly, a 64-lead electroencephalogram cap and an electromyogram collector collect 10-channel electroencephalogram signals and surface electromyogram signals of ulnar carpal flexor muscle, flexor superficial muscle and radial carpal flexor muscle; and a brain muscle function network is established by combining symbolic transfer entropy and graph theory knowledge, and network feature vectors are extracted to be used for training a classifier through a K nearest neighbor algorithm and finally used for detecting exercise fatigue. According to the method, the defect that in a traditional exercise fatigue detection method based on bioelectricity signals, brain-muscle cooperation is not comprehensively considered for exercise control is overcome, the electroencephalogram signals and the electromyogram signals are combined for exercise fatigue detection, and the result shows that the method has high accuracy in exercise fatigue detection.

Description

technical field [0001] The invention belongs to the field of pattern recognition and relates to a detection method for human exercise fatigue, in particular to a detection method for exercise fatigue based on electroencephalogram and electromyographic signals. Background technique [0002] Muscle fatigue is defined as a decreased ability to maintain a certain strength during continuous contractions, or failure to achieve initial strength levels during intermittent contractions, which is reflected in the electrical activity of the muscle. This term refers to feeling more difficult or requiring more effort than expected. In healthy people, repeated or sustained muscle activation can lead to fatigue, which is common during exercise or everyday life. The most obvious effect is a reduction in the muscle's ability to generate force, which increases the likelihood of muscle injury. This manifestation is also strongly associated with patients with motor dysfunction such as stroke ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/06G06F2218/08G06F2218/12G06F18/23G06F18/214G06F18/24
Inventor 席旭刚皮少军赵云波孔万增马玉良张启忠罗志增
Owner HANGZHOU DIANZI UNIV
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