Abstract:
Human walking pattern is believed to be unique in its nature and it is well-known through
a number of previous research that soft biometrics of humans are encoded in sparse
kinematic information, or in other words, in human walking pattern. These kinematic
information can now be recorded using inertial measurement units, thanks to the
advancement of modern digital devices. In most cases, body-mounted digital devices are
used to capture a subject's movement. The raw signals are used to learn and estimate the
encoded soft biometrics, which are present in the captured data. We developed a deep
learning based algorithm to learn various soft biometrics about a human, such as gender
and age from a single step from the recorded data of the person. In this study, we show
how a single step of a human gait may be used to determine gender and age. We acquired
6D angular velocities and accelerations of 86 volunteers with the help of chest-mounted
inertial measurement units as they conducted their normal gait tasks. Big sequences of
signals were broken down into separate step data by subjecting the data to
segmentation before being fed to the model. We were successful in predicting a person's
gender with a maximum accuracy of 100 % for males and 99.32 % for females. The root
mean squared error for age prediction was only 2.29 years, the mean squared error was
only 5.25 years and mean absolute error of 0.29 years. We have got the precision score
of 0.96, recall score of 0.975 and F1 score of 0.976 for age estimation.
