Classifying the walking pattern of humans on different surfaces using convolutional features and shallow machine learning classifiers

Abstract

This study presents a methodology that combines convolution features with shallow classifiers for classifying the walking pattern on different surfaces. At first, convolution features are extracted from six different inertial measurement units (IMU) sensors mounted on the human body. The shallow classifiers namely quadratic SVM, wide neural network, fine KNN, and linear discriminant analysis are trained using convolution features that successfully pass through the global pooling layer of the CNN model. The proposed methodology is also evaluated on the features extracted from both individual IMU sensor and combined IMU sensors. It is observed that proposed methodology performs better for convolution features extracted from all IMU sensors combined together. The proposed methodology is also compared to the CNN model to demonstrate its efficacy. The obtained results show that the shallow classifier (quadratic SVM) achieves the maximum accuracy of 98.2%, whereas the CNN model achieves the accuracy of 90.73% only. Thus, the proposed methodology can be successfully utilised for classifying different walking surfaces based on the gait cycle data. The proposed approach can benefit in automatic gait adjustment of prosthetic foot based on the walking surfaces.