Muscle strength and muscle fatigue prediction algorithm based on surface electromyogram signals

Abstract

The invention relates to a muscle strength and muscle fatigue prediction algorithm based on surface electromyogram signals, which comprises the following steps of: (1) acquiring the surface electromyogram signals of a measured muscle area by using a non-invasive sensor, and acquiring muscle mechanics parameters at the same time; (2) carrying out filtering, amplification and noise reduction preprocessing on the collected surface electromyogram signals to eliminate interference and enhance signal quality; and (3) extracting multi-dimensional features from the preprocessed surface electromyogram signals and muscle mechanics parameters, establishing a frequency domain data set by using the feature values, and calibrating the muscle mechanics parameters. The method has the advantage that the prediction accuracy and reliability are enhanced. Muscle strength and muscle fatigue states of different individuals can be reflected more accurately, and accurate prediction of individual differences is realized. The adopted surface electromyogram signal acquisition mode is non-invasive, and a plug-in electrode is not needed, so that the test discomfort is reduced, and the acceptability and participation degree of a testee are increased; by establishing an individual model and combining with real-time signal acquisition, real-time monitoring and prediction of muscle strength and muscle fatigue can be realized, and a user can obtain feedback of a muscle state at any time, so that the exercise intensity, posture and training plan are adjusted, and the training effect is optimized.

Images