A kind of fitness assistant method based on myoelectric signal

Abstract

The invention relates to a body-building assistance method based on an electromyography signal, which belongs to the technical field of pattern recognition and artificial intelligence, and relates to a body-building assistance method based on an electromyography signal. In this method, the electromyographic signals of the human body surface are collected through the electrode sheet, and the electromyographic signals of the human body are processed through various acquisition circuits, and the processed electromyographic signals are transmitted to the host computer, and the characteristic values ​​of the signals are extracted. The SVM support vector machine through machine learning is used as a classifier model to determine whether the action is standard. The linear kernel and the Gaussian kernel with different λ parameters are used to train the SVM model respectively, and the model with the best training effect is selected to determine the standard fitness action. A three-layer BP neural network is used as a classifier model to classify actions, and the extracted feature values ​​are used as input neurons of the neural network. The RELU function is used as the activation function to realize the non-linear mapping of the input information. After training, the classifier model is obtained to realize the action recognition, which has a high accuracy rate.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and artificial intelligence, and relates to a body-building assistance method based on electromyographic signals. Background technique [0002] Fitness can help prevent many diseases. In recent years, more and more people have realized the importance of fitness. The fitness industry has developed rapidly and penetrated into people of all ages. However, non-standard fitness movements will make the fitness effect poor, and in severe cases, it will cause muscle strain. Additionally, further research has shown that users are more motivated during training if the workout is supervised. Therefore, it is of great significance to study the fitness aid method. [0003] Current fitness assistance methods are mainly based on body posture, in Depari A, Ferrari P, Flammini A, Rinaldi S. Lightweight Machine Learning-Based Approach for Supervision of Fitness Workout[C] / / IEEE Conference Sensors Appl...

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Problems solved by technology

[0003] Current fitness assistance methods are mainly based on body posture, in Depari A, Ferrari P, Flammini A, Rinaldi S. Lightweight Machine Learning-Based Approach for Supervision of Fitness Workout[C] / / IEEE Conference Sensors Applications Symposium.Institute of Electrical and Electronics Engineers Inc , 2019. In this document, fitness assistance is provided through the posture of the x, y, and z axes, which can assist the fitness within a certain error tolerance range, but due to the large amount of posture information and the large amount of disturbed data, errors are prone to occur. Judgment, and the specific state of the muscles cannot be directly reflected with the help of posture information, and the reliability of the assistance is low

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