A Multimodal Dataset for Gait Recognition in Different Terrains using Wearable Sensors

Abstract

Gait has been shown to be a profound movement in human activities, and gait recognition is a commonly used biometric recognition in recent years. Gait recognition based on wearable sensors has been involved in various application areas. Especially in the area of medical, gait research is an essential issue. The purpose of this paper is to provide a multimodal public dataset for use with gait recognition. The dataset is derived of data from wearable inertial sensors and ECG sensor. Both sensors provide easy-to-operate and low-cost data recording for gait recognition. The gait dataset is based on the data from 15 healthy adults whose lower limbs have neither been injured nor operated on in the past year. Unlike other well-known datasets in the literature, this dataset contains inertial data (built-in gyroscope, accelerometer, geomagnetic field sensor) recorded from the ankle, as well as ECG data from a cardiac sensor. In this paper, the 15 volunteers were asked to walk at their most comfortable pace in four different terrains and complete the test. These four kinds of terrains are: flat land, sand, grassland and blind road. In addition, in order to verify the effectiveness of this multimodal dataset, this paper uses deep learning to identify the gait patterns of four terrains, and the recognition rate reaches 82%.