DeepSense-Inception: Gait Identification from Inertial Sensors with Inception-like Architecture and Recurrent Network

Abstract

Gait recognition has been considered as a new promising approach for biometric-based authentication. Gait signals are commonly obtained by collecting motion data from inertial sensors (accelerometers, gyroscopes) integrated in mobile and wearable devices. Motion data is subsequently transformed to a feature space for recognition procedure. One fashionable, effective way to extract features automatically is using conventional Convolutional Neural Networks (CNN) as feature extractors. In this paper, we propose DeepSense-Inception (DSI), a new method inspired from DeepSense, to recognize users from their gait features using Inception-like modules for better feature extraction than conventional CNN. Experiments for user identification on UCI Human Activity Recognition dataset demonstrate that our method not only achieves an accuracy of 99.9%, higher than that of DeepSense (99.7%), but also uses only 149K parameters, less than one third of the parameters in DeepSense (529K parameters). Thus, our method can be implemented more efficiently in limited resource systems.