Applications of human action analysis and recognition on wireless network infrastructures: State of the art and real world challenges

Abstract

Human action recognition and analysis has given life to a wide variety of real-world applications, ranging from surveillance and human-computer interaction to patient monitoring and rehabilitation. Most action recognition systems, especially smart-home or assistive living applications, depend on network infrastructures for easy data fusion and integration of different sensing modalities. However, despite the fact that action recognition methods have extensively been evaluated for their accuracy and there is a consensus on the ways to provide quality of service in various network infrastructures, there is poor coverage of the inherent challenges of performing human action in real world network-based applications. In this work, we attempt to document these challenges based on representative, state of the art techniques and venture to report on the open issues that need to be resolved by new techniques aiming to provide viable real world applications.