The RELATE interaction model is designed to support spontaneous interaction of mobile users with devices and services in their environment. The model is based on spatial references that capture the spatial relationship of a user's device with other co-located devices. Spatial references are obtained by relative position sensing and integrated in the mobile user interface to spatially visualize the arrangement of discovered devices, and to provide direct access for interaction across devices. In this paper we discuss two prototype systems demonstrating the utility of the model in collaborative and mobile settings, and present a study on usability of spatial list and map representations for device selection.

If a mobile computing device knows how it is positioned and oriented in relation to other devices nearby, then it can provide enhanced support for multi-device and multi-user interactions. Existing systems that provide position information to mobile computers are reliant on externally deployed infrastructure, such as beacons or sensors in the environment. We introduce the Relate system, which provides fine-grained relative position information to co-located devices on the basis of peer-to-peer sensing, thus overcoming dependence on any external infrastructure. The system is realised as a hardware/software plug-in, using ultrasound for peer-to-peer sensing, USB to interface with standard mobile devices, and data abstraction and inferencing to map sensor data to a spatial model that maintains both quantitative and qualitative relationships. We present a set of services and applications to demonstrate the utility of the system. We report experimental results on the accuracy of the relative position and orientation estimates, and other aspects of system performance.

Ubiquitous computing is giving rise to applications that interact very closely with activity in the real world, usually involving instrumentation of environments. In contrast, we propose Cooperative Artefacts that are able to cooperatively assess their situation in the world, without need for supporting infrastructure in the environment The Cooperative Artefact concept is based on embedded domain knowledge, perceptual intelligence, and rule-based inference in movable artefacts. We demonstrate the concept with design and implementation of augmented chemical containers that are able to detect and alert potentially hazardous situations concerning their storage.

Ambient intelligent applications require applications to recognise user activity calmly in the background, typically by instrumentation of environments. In contrast, we propose the concept of Cooperative Artefacts (CAs) to instrument single artefacts that cooperate with each other to acquire knowledge about their situation in the world. CAs do not rely on external infrastructure as they implement their architectural components, i.e. perceptual intelligence, domain knowledge and a rule-based inference engine, on embedded devices. We describe the design and implementation of the CA concept on an embedded systems platform and present a case study that demonstrates the potential of the CA approach for activity recognition. In the case study we track surface-based activity of users by augmenting a table and household goods.

In this paper we describe a new method and user interface for interactive positioning of a mobile device. The key element of this method is a question-answer style dialogue between system and user about the visibility of nearby objects and landmarks; answers given by the user provide clues about the relative position of the user and allow the verification or falsification of hypotheses about the user's absolute location. This new approach combines the respective strengths of a human user (i. e. fast and reliable object recognition) and a mobile system (i. e. fast computation of numerical data). It enables accurate positioning without requiring any other positioning technologies. A particular advantage of this approach is that it lends itself to the implementation on camera-equipped mobile phones, where it can be used to increase the accuracy of cell-based localisation methods.