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.