Gait anomaly recognition method based on back-propagation neural network

Abstract

The invention belongs to the technical field of biometric feature recognition, and particularly relates to a gait anomaly recognition method based on a back-propagation neural network. The method comprises the steps that an IMU worn by the human body is used for collecting signals when the human body walks normally and when typical abnormal gait walking is simulated to obtain triaxial accelerationinformation under different gaits; original data is subjected to windowing cutting pretreatment according to a target typical walking stride frequency, and each data queue is labeled correspondinglyaccording to gait classes; a BNPP back-propagation neural network is constructed; obtained data tag pairs are classified into a training set and a test set, the training set is sent to a BPNN for training, and after training is completed, the test set is used for evaluating a model classification effect. The gait anomaly recognition method based on the back-propagation neural network has the advantages that the original IMU triaxial acceleration data is directly classified by increasing the number of input layer nodes, thereby eliminating the complicated gait cycle division and feature extraction engineering, increasing the classification accuracy rate of various abnormal gaits, reducing the workload of data pre-processing and improving the classification accuracy.

Description

technical field [0001] The invention belongs to the technical field of biological feature identification, and in particular relates to a method for identifying abnormal gait. Background technique [0002] Gait refers to the posture shown when people walk, and is one of the important biological characteristics of the human body. Abnormal gait is mostly related to the lesion. As an important feature reflecting the health status and behavior ability of the human body, accurate and credible gait information can be obtained in time, and the abnormal gait classifier can be trained to give timely early warning of abnormal gait, and it can be monitored for a long time. Monitoring and evaluation have important guiding significance in medical diagnosis and disease prevention. [0003] The current mainstream gait recognition methods are mainly divided into computer vision solutions based on video and image processing and sensor solutions based on walkways and wearable sensors such as ...

AI Technical Summary

Problems solved by technology

Both schemes involve a large amount of professional data preprocessing and complicated feature engineering after obtaining the original data in order to extract the relevant features in the gait cycle. Although the accuracy is high, the real-time performance is poor and the computational complexity is high.

The mainstream gait recognition system only provides various numerical indicators, and the identification and classification tasks of abnormal gait are mainly completed by human experts, which requires a lot of professional knowledge in related fields

Images