Abstract
Human activity recognition in Ambient Assisted Living (AAL) is an important application in health care systems and allows us to track regular activities or even predict these activities in order to monitor healthcare and find changes in patterns and lifestyles. A review of the literature reveals various approaches to discovering and recognizing human activities. The presence of a vast number of activity recognition issues and approaches has made it difficult to make adequate comparisons and accurate assessment. Introducing the five basic components of activity recognition in the smart homes as a famous environment to remote monitoring of patients and independent living for elderly, the present paper proposes SARF framework to classify each of activity recognition approaches and then it is evaluated based on the proposed classification by some proposed measures. Using SARF proposed framework can play an effective role in selecting the appropriate method for human activity recognition in smart homes and beneficial in analysis and evaluation of different methods for various challenges in this field.
Highlights
IntroductionI N RECENT years automatic human activity recognition has received considerable attention due to the growing demand in many applications such as healthcare systems for monitoring the Activities of Daily Living (ADL) in smart homes, especially due to the rapid growth of elderly population, in surveillance and security environments to automatic detection of abnormal activities to alert the relevant authorities about the potential criminal or terrorist behavior, in activity-aware services to convert ideas like smart meeting rooms, home automation, personal digital assistants from science fiction to everyday fact and in entertainment environments to improve human interaction with computers [1][2][3].Due to the many uses of activity recognition in smart homes and the availability of various approaches in this field, comparison and accurate evaluation of existing methods is difficult
There are a large number of Activities of Daily Living (ADL) in a variety of categories which can all be modeled at multiple levels of granularity [3]
Most ADLs involve performing a number of actions
Summary
I N RECENT years automatic human activity recognition has received considerable attention due to the growing demand in many applications such as healthcare systems for monitoring the Activities of Daily Living (ADL) in smart homes, especially due to the rapid growth of elderly population, in surveillance and security environments to automatic detection of abnormal activities to alert the relevant authorities about the potential criminal or terrorist behavior, in activity-aware services to convert ideas like smart meeting rooms, home automation, personal digital assistants from science fiction to everyday fact and in entertainment environments to improve human interaction with computers [1][2][3].Due to the many uses of activity recognition in smart homes and the availability of various approaches in this field, comparison and accurate evaluation of existing methods is difficult. The main contribution of this paper, after briefly introducing five basic components of human activity recognition in smart homes, is proposing SARF framework to classify different methods in this field. This framework is analyzed in terms of approaches, their characteristics, challenges and proposed measures. The remainder of this paper is organized as follows: In Section II, basic definition for human activity recognition and its capabilities in healthcare systems will be introduced.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
