Architectures for Activity Recognition and Context-Aware Computing

The last 10 years have seen the development of novel architectures and technologies for domainfocused, task-specific systems that know many things, such as who (identities, profile, history) they are with (social context) and in what role (responsibility, security, privacy); when and where (event, time, place); why (goals, shared or personal); how are they doing it (methods, applications); and using what resources (device, services, access, and ownership). Smart spaces and devices will increasingly use such contextual knowledge to help users move seamlessly between devices and applications, without having to explicitly carry, transfer, and exchange activity context. Such systems will qualitatively shift our lives both at work and play and significantly change our interactions both with our physical and virtual worlds. This dream of seamlessly interacting with our virtual environment has a long history as can be seen in Apple Inc.'s Knowledge Navigator 1987 concept video. However, the combination of dramatic progress in low-power mobile computing devices and sensors, with advances in artificial intelligence and human-computer interaction (HCI) in the last decade, have provided the kind of platforms and algorithms that are enabling context-aware virtual personal assistants that plan activities and recognize intent. This has lead to an increase in work designed to bring these ideas into real world application and address the final technical hurdles that will make such systems a reality.