  Human activity recognition (HAR) is an essential research field that has been
used in different applications including home and workplace automation,
security and surveillance as well as healthcare. Starting from conventional
machine learning methods to the recently developing deep learning techniques
and the Internet of things, significant contributions have been shown in the
HAR area in the last decade. Even though several review and survey studies have
been published, there is a lack of sensor-based HAR overview studies focusing
on summarising the usage of wearable sensors and smart home sensors data as
well as applications of HAR and deep learning techniques. Hence, we overview
sensor-based HAR, discuss several important applications that rely on HAR, and
highlight the most common machine learning methods that have been used for HAR.
Finally, several challenges of HAR are explored that should be addressed to
further improve the robustness of HAR.
