  Mobile and wearable devices have enabled numerous applications, including
activity tracking, wellness monitoring, and human--computer interaction, that
measure and improve our daily lives. Many of these applications are made
possible by leveraging the rich collection of low-power sensors found in many
mobile and wearable devices to perform human activity recognition (HAR).
Recently, deep learning has greatly pushed the boundaries of HAR on mobile and
wearable devices. This paper systematically categorizes and summarizes existing
work that introduces deep learning methods for wearables-based HAR and provides
a comprehensive analysis of the current advancements, developing trends, and
major challenges. We also present cutting-edge frontiers and future directions
for deep learning-based HAR.
