The invention belongs to the technical field of biometric recognition, and particularly relates to a
gait abnormality classification method based on a deep
convolution neural network. The method includes the following steps that a IMU worn on
human body is used for collecting signals of normal walking and simulating typical abnormal
gait walking, and triaxial acceleration information under different gaits is obtained; according to the typical walking frequency of a target,
original data is subjected to windowing
cutting pretreatment, and each data
queue is correspondingly labeled according tothe
gait type, wherein the CNN deep
convolution neural network includes a first
convolution layer, a second convolution layer, a first
pooling layer, a second
pooling layer, a full connection layer and a soft max output layer; finally, data labels are divided into a
training set and a
test set, the
training set is sent to the CNN for training, and the
test set is used for evaluating the model classification effect after training. The method omits complicated
gait cycle division and
feature extraction engineering, improves the classification accuracy of various
abnormal gaits, reduces the
workload of data preprocessing, and improves the classification accuracy.