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Muscle fatigue dynamic prediction method based on multi-channel sEMG

A muscle fatigue and dynamic prediction technology, applied in diagnostic recording/measurement, medical science, sensors, etc., can solve problems such as incomplete conclusions, difficulty in muscle fatigue prediction, etc., to predict muscle fatigue time, reduce external interference, and improve computing speed effect

Inactive Publication Date: 2015-12-16
NINGBO UNIVERSITY OF TECHNOLOGY
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Problems solved by technology

[0004] Because the sEMG signal of muscle fatigue is affected by the following factors: exercise mode, exercise intensity, muscle contraction mode (centrifugal, concentric contraction), muscle type, individual characteristics, etc., in many researches on fatigue EMG characteristics, different characteristics The parameters are not completely consistent, and the prediction of muscle fatigue is still difficult

Method used

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  • Muscle fatigue dynamic prediction method based on multi-channel sEMG
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  • Muscle fatigue dynamic prediction method based on multi-channel sEMG

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Embodiment Construction

[0030] The following describes the implementation of the present invention through specific specific examples, and those skilled in the art can easily implement the content disclosed in this specification.

[0031] Such as figure 1 As shown, a method for dynamic prediction of muscle fatigue based on multi-channel sEMG includes the following steps:

[0032] 1): Preprocessing the multi-channel sEMG signal: re-select the reference value, and use band pass filter and band stop filter to eliminate interference. The specific steps are as follows:

[0033] a) will Channel sEMG signal Add and average ;

[0034] b) Use the above average value as a reference value to obtain a new Channel sEMG signal, , ,..., ;

[0035] c) the new Channel sEMG signal Perform band-pass filtering and band-stop filtering to eliminate interference, and subsequent sEMG signal processing is aimed at the new Channel sEMG signal.

[0036] The band pass filter is used to retain the signal in the 10Hz-500Hz freque...

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Abstract

The invention provides a muscle fatigue dynamic prediction method based on multi-channel sEMG. An sEMG signal is susceptible to external interference, so an average value of all channels is selected to be a reference value; a difference is made between the new reference value and the original multi-channel sEMG, so external interference can be effectively reduced; a time-scale least square method is translated according to time reference to achieve dynamic update and predict muscle fatigue time; MPF and MF parameters are important parameters reflecting muscle fatigue, so reliability is provided for muscle fatigue prediction; the MPF and the MF two parameters are employed for prediction of muscle fatigue time, so instability of single parameter prediction can be avoided and prediction robustness can be enhanced; and the muscle fatigue dynamic prediction method based on multi-channel sEMG has high accuracy, fast calculation speed, simplicity and important application values.

Description

Technical field [0001] The invention relates to a method for dynamic prediction of muscle fatigue based on multi-channel sEMG. Background technique [0002] Research on the mechanism and prediction of neuromuscular fatigue is a hot spot in sports medicine research at home and abroad, and it is also the focus of scientific research on sports humans. During exercise, due to the oxygen supply in the blood or lack of nutrients, a series of changes will occur in the structure, metabolism and energy of the muscles, which will reduce the efficiency of the neuromuscular system, so that the muscles cannot continue to complete tasks, resulting in Muscle fatigue. Muscle fatigue may cause muscle damage, and in severe cases, muscle fatigue cannot be recovered. The research on muscle fatigue has broad application prospects in the fields of ergonomics, human-machine interface, rehabilitation medicine, sports injuries, and artificial limbs. [0003] At present, the clinical detection tools for ...

Claims

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Application Information

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IPC IPC(8): A61B5/00
Inventor 何金保骆再飞易新华廖远江
Owner NINGBO UNIVERSITY OF TECHNOLOGY
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