  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.
