Gait recognition is the characterization of unique biometric patterns associated with each individual which can be utilized to identify a person without direct contact. A public gait database with a relatively large number of subjects can provide a great opportunity for future studies to build and validate gait authentication models. The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints (320 meters) during two different sessions and record their gait data using two smartphones, one attached to the right thigh and another one on the left side of the waist. This data is collected to be utilized by a deep learning-based method that requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available.

翻译：步态识别是利用与每个人关联的独特生物特征模式进行个体识别的一种非接触式方法。一个包含较多受试者的公开步态数据库，可为未来构建和验证步态认证模型的研究提供重要机会。本研究旨在建立一个包含93名受试者的综合性步态数据库，受试者在两次不同时段内完成两点间（320米）行走，并利用两部智能手机（分别固定于右大腿和左侧腰部）记录其步态数据。该数据采集旨在支持需要足够时间点的深度学习方法。元数据涵盖受试者的年龄、性别、吸烟习惯、每日运动时长、身高和体重信息。本数据集已公开提供。