  In the last decade, Human Activity Recognition (HAR) has become a vibrant
research area, especially due to the spread of electronic devices such as
smartphones, smartwatches and video cameras present in our daily lives. In
addition, the advance of deep learning and other machine learning algorithms
has allowed researchers to use HAR in various domains including sports, health
and well-being applications. For example, HAR is considered as one of the most
promising assistive technology tools to support elderly's daily life by
monitoring their cognitive and physical function through daily activities. This
survey focuses on critical role of machine learning in developing HAR
applications based on inertial sensors in conjunction with physiological and
environmental sensors.
