In the last decade, Human Activity Recognition (HAR) has become a very important 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 (DL) has led researchers to use HAR in various domains including  health and well-being applications. HAR is one of the most promising assistive technology tools to support elderly's daily life . However, this class of algorithms requires large amounts of data. Furthermore, not all the HAR application fields generate a significant amount of data and not all of them provide the computational power that DL models in HAR require . This survey focuses on critical applications of Machine Learning (ML) in the fields of HAR , largely oriented to Daily Life Activities by presenting an overview of the publications on HAR based on ML and inertial , physiological and environmental sensors.
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.
R
very important
vibrant
R
Deep Learning (DL) has led
deep learning and other machine learning algorithms has allowed
A
sports,
R
HAR is
For example, HAR is considered as
R
. However, this class of algorithms requires large amounts of data. Furthermore, not all the HAR application fields generate a significant amount of data and not all of them provide the computational power that DL models in HAR require
by monitoring their cognitive and physical function through daily activities
R
applications of Machine Learning (ML) in the fields of HAR , largely oriented to Daily Life Activities by presenting an overview of the publications on HAR based on ML and inertial ,
role of machine learning in developing HAR applications based on inertial sensors in conjunction with