Data Augmentation for Inertial Sensor-Based Gait Deep Neural Network

Abstract

Inertial sensor-based gait has been discovered as an attractive method for user recognition. Recently, with the approaching of deep learning techniques, new state-of-the-art researches have been established. However, the scarcity of training data still endures as an obstacle that impedes to build a robust deep gait model. In this study, we address that problem by proposing a novel approach for inertial sensor-based gait data augmentation. First, two label-preserving transformation algorithms, namely Arbitrary Time Deformation (ATD) and Stochastic Magnitude Perturbation (SMP), are proposed to generate more training data from the real gait data. The ATD algorithm adjusts the timing information of gait data with random values, on the other hand, SMP alters the magnitude arbitrarily, to create variations on the augmenting data. Then, we design a generic gait recognition model using convolutional neural network, in which, the ATD and SMP algorithms are coordinated appropriately to produce augmenting data varied naturally in both time and magnitude as real data. The proposed approach was evaluated on two public datasets, one was collected in unconstrained conditions, and the other had the largest number of participating users. The experiment showed that, under different amounts of training data, using ATD or SMP alone could increase the recognition performance effectively, and their combination even attained higher accuracy. With ATD and SMP, our model achieved competitive performance on both two datasets comparing to state-of-the-art researches.