A review of machine learning-based human activity recognition for diverse applications

摘要

Human activity recognition (HAR) is a very active yet challenging and demanding area of computer science. Due to the articulated nature of human motion, it is not trivial to detect human activity with high accuracy for all applications. Generally, activities are recognized from a series of actions performed by the human through vision-based sensors or non-vision-based sensors. HAR's application areas span from health, sports, smart home-based, and other diverse areas. Moreover, detecting human activity is also needed to automate systems to monitor ambient and detect suspicious activity while performing surveillance. Besides, providing appropriate information about individuals is a necessary task in pervasive computing. However, identifying human activities and actions is challenging due to the complexity of activities, speed of action, dynamic recording, and diverse application areas. Besides that, all the actions and activities are performed in distinct situations and backgrounds. There is a lot of work done in HAR; finding a suitable algorithm and sensors for a certain application area is still challenging. While some surveys are already conducted in HAR, the comprehensive survey to investigate algorithms and sensors concerning diverse applications is not done yet. This survey investigates the best and optimal machine learning algorithms and techniques to recognize human activities in the field of HAR. It provides an in-depth analysis of which algorithms might be suitable for a certain application area. It also investigates which vision-based or non-vision-based acquisition devices are mostly employed in the literature and are suitable for a specific HAR application.