Gait Database

Gait recognition is the characterization of unqiue biometric patterns associated with each inidvidual which can be utilized to identify a person without direct contact. A public gain database with relatively large number of subjects can provide a great oppportunity to 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 end points (320 meters) during two different sessions and record their gait data using two smart phones, one was attached to right thigh and another one on left side of waist. This data is collected with intention to be utilized by deep learning-based method which requires enough time points. The meta data including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available. <br> Except 19 subjects who did not attend for second session, every subject is associated with 4 different log files (each session contains two log files). Every file name has one of the following patterns: · sub0-lw-s1.csv: subject number 0, left waist, session 1 · sub0-rp-s1.csv: subject number 0, right thigh, session 1 · sub0-lw-s2.csv: subject number 0, left waist, session 2 · sub0-rp-s2.csv: subject number 0, right thigh, session 2 Every log file contains 58 features that are internally captured and calculated using SensorLog app. Additionally, an Excel file contain the meta data is provided for each subject.

步态识别（Gait recognition）是指对与每个个体相关的独特生物特征模式进行表征，可在无需直接接触的前提下完成身份识别。拥有相对较多受试者的公开步态数据库，能够为未来构建与验证步态身份验证模型的相关研究提供绝佳契机。本研究旨在介绍一个涵盖93名人类受试者的综合性步态数据库：受试者需在两次不同的采集会话中，于两个端点间行走（距离320米），并通过两部智能手机采集步态数据——一部绑定于右侧大腿，另一部佩戴于腰部左侧。本数据集的采集初衷是供需要充足时间序列数据的深度学习方法使用。研究人员同时记录了受试者的年龄、性别、吸烟习惯、每日运动时长、身高与体重等元数据。本数据集可公开获取。
除19名未参与第二次采集会话的受试者外，每名受试者均对应4个不同的日志文件（单次会话包含2个日志文件）。每个文件名均遵循以下格式之一：
· sub0-lw-s1.csv：受试者0号，腰部左侧，会话1
· sub0-rp-s1.csv：受试者0号，右侧大腿，会话1
· sub0-lw-s2.csv：受试者0号，腰部左侧，会话2
· sub0-rp-s2.csv：受试者0号，右侧大腿，会话2
每个日志文件包含58项由SensorLog应用内部采集并计算得到的特征。此外，还为每名受试者提供了包含其元数据的Excel文件。