Abstract
The various activities of human movements have been discussed for several years, such as sports activities, daily life activities, and so on. Their detection and classification have given crucial information about a person's behaviour and health status. So, there has always been a purpose for detecting and classifying these activities for real-life problems. Behavioural recognition, fall detection, intrusion detection, human health prediction model, ambulatory monitoring, smart access to electronic appliances, etc., are the main motives of the detection of physical activity in the context of daily life. Nowadays, various types of wearable sensors are available in tiny sizes due to the advancements in miniature technology in electronic devices, which proved very useful for detecting human motions. Here in this article, some important methodologies, physical activity basics, and their classification using machine learning and deep learning approaches are discussed in the context of wearable sensors. After reading this article, the researcher could summarise the whole theory and technical aspects of activity recognition. Wearable sensors have gained tremendous traction for sensing human motion due to their various advantages over other sensors.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
