  Human gait has been shown to provide crucial motion cues for various
applications. Recognizing patterns in human gait has been widely adopted in
various application areas such as security, virtual reality gaming, medical
rehabilitation, and ailment identification. Furthermore, wearable inertial
sensors have been widely used for not only recording gait but also to predict
users' demography. Machine Learning techniques such as deep learning, combined
with inertial sensor signals, have shown promising results in recognizing
patterns in human gait and estimate users' demography. However, the black-box
nature of such deep learning models hinders the researchers from uncovering the
reasons behind the model's predictions. Therefore, we propose leveraging deep
learning and Layer-Wise Relevance Propagation (LRP) to identify the important
variables that play a vital role in identifying the users' demography such as
age and gender. To assess the efficacy of this approach we train a deep neural
network model on a large sensor-based gait dataset consisting of 745 subjects
to identify users' age and gender. Using LRP we identify the variables relevant
for characterizing the gait patterns. Thus, we enable interpretation of
non-linear ML models which are experts in identifying the users' demography
based on inertial signals. We believe this approach can not only provide
clinicians information about the gait parameters relevant to age and gender but
also can be expanded to analyze and diagnose gait disorders.
