Lower-limb flat ground walking gait recognition method based on GA-BP (Genetic Algorithm-Back Propagation) neural network

Abstract

The invention discloses a lower-limb flat ground walking gait recognition method based on a GA-BP neural network. The method comprises the following steps: performing denoising smoothing and time domain feature parameters extraction to the acquired lower-limb continuous flat ground waking four-way surface electromyogram signals to obtain a feature value sample set; then optimizing the BP neural network with the GA to obtain a group of complete initial weight values and threshold values with the minimum BP neural network deviation; randomly dividing the extracted feature values into a training sample group and a test sample group, and using the training samples to train the GA optimized BP neural network; at last, inputting the test sample in the trained BP neural network classifier to perform recognition and classification. By virtue of the lower-limb flat ground walking gait recognition method, the time domain features of the electromyogram signals are easy to extract and obvious and has good expression capability.

Description

technical field [0001] The invention relates to a human body motion pattern recognition method, in particular to a GA-BP neural network gait recognition method based on the eigenvalues ​​of electromyography signals when the lower limbs walk on level ground. Background technique [0002] Gait reflects the posture of the lower limbs walking movement, and is a general term for the walking state of the lower limbs. It has an important relationship with the structure and function of the human body, movement coordination organization, behavior and psychological activities, and is the most basic action in the life activities of the human body. Normal gait refers to the gait in which the lower limbs of a healthy human being walk in the most natural and comfortable posture. It has the characteristics of periodicity, coordination and balance. [0003] The human body surface EMG signal is a low-frequency weak biological signal, which is essentially a non-stationary and non-Gaussian ph...

AI Technical Summary

Problems solved by technology

However, in practical problems, the number of samples is often limited, so the performance of these classification methods with significant advantages in theory may not be satisfactory in practical applications. For example, the traditional BP neural network classification is prone to local minima and the classification effect is not good. ideal

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