Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey
It provides a comprehensive review for researchers and practitioners in HAR, but is incremental as a survey.
This survey examines the role of machine learning in developing human activity recognition applications using inertial, physiological, and environmental sensors, highlighting its potential in domains like health and elderly care.
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.