A survey of context-aware middleware designs for human activity recognition

Abstract

The ever increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor designs and deployment, have enabled smart devices to recognize daily human-based actions, activities, and interactions. Therefore, inferring a vast variety of user-device-based activities from diverse contexts obtained by a series of sensory observations has drawn much interest to the research area of ubiquitous sensing. The existence and awareness of context provides the capability of being conscious of physical environments or situations around users, and this allows network services to respond proactively and intelligently based on such awareness. Hence, with the evolution of smartphones, software developers have been empowered to create context-aware applications for recognizing human-centric or community based innovative social and cognitive activities in any situation and at any location. However, the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth compared to the capabilities of PCs and servers. Also, power is a major restriction on implementation of context-aware applications. Mobile device batteries do not last a long time while operating sensor(s) constantly. To this end, this article extensively surveys the emerging concepts of context awareness in mobile platforms by providing up-to-date literature and future research directives. We also point out the challenges faced in this regard and enlighten them by offering possible solutions.