Towards generic human activity recognition for ubiquitous applications

Abstract

Ubiquitous computing presents nowadays a new paradigm of computation where computers are embedded into the everyday environment. The vanishing of computers in the environment can be obtained through sophisticated and miniaturized devices such as wearable sensors, unobtrusive sensor networks and computer vision technologies. Nowadays, thanks to the low cost, small size and high computational power of these devices, pervasive sensors are able to interact autonomously with humans as a part of their day-to-day. One of the most fundamental areas of research that builds on top of Ubiquitous Applications is the making of human-centric intelligent spaces. As a difference with other intelligent spaces, they focus on creating a context-sensitive computing with respect to humans. That is to say, this intelligence provides inference mechanisms regarding humans’ conditions, feelings, actions or activities. This inference is noteworthy in order to improve the ubiquitous interaction between humans and electronic devices which surround them. Hence, the overall success rate of these systems strongly depends on the inference from the context. In other words, ubiquitous devices should be context-aware. This special issue is focused on one of the most important inference or context-aware systems in Ubiquitous applications (among other such as location, natural language processing, emotional computing, etc.) that is Human Activity Recognition (HAR). In this human activity awareness it is possible to distinguish between different levels of recognition regarding the complexity of activities they tackle: (1) gestures; (2) individual actions; (3) humanobject interactions; (4) human–human interactions; (5) complex or composite activities. HAR systems have become a very prominent area of research but more especially in fields such medical, security and military. Thus, in computing there have been advances in HAR since the late 90’s. Nevertheless, there are still many open issues to overcome in this purpose. From the hardware perspective, there is still an intensive area of research to improve the portability, price, network communication, processing capacities and energy efficiency. Hardware devices can be classified between external (such as intelligent homes) and wearable devices. External devices have the capacity of recognizing long complex activities that comprise several actions such as ‘‘preparing the dinner’’, ‘‘cutting the grass’’, ‘‘playing console games’’ etc. The recognition of these activities depends heavily on the knowledge extraction from several sensors (locational, tag, cameras, microphones etc.). Such environments with many sensors are so-named intelligent homes or smart homes. The problem in this type arises when users do not necessarily interact (for example, they are out of range) with those sensors. Besides, some specific sensors such as video cameras entail further issues such as privacy and scalability. From the software perspective, there are still many challenges such as: (1) the design of an optimal feature extraction; (2) real-time and scalable inference systems; (3) low computational and embeddable algorithms; (4) support to new users without the need of re-training. In this special issue are presented state-of-the-art advances from four European specialized research centers in computing. Three out of four of these pieces of research focus on improvement of the intelligent system J. Andreu (&) P. Angelov School of Computing and Communications, Lancaster University, Lancashire, UK e-mail: j.andreu@lancaster.ac.uk