We have created a system for identifying people based on their footstep force profiles and have tested its accuracy against a large pool of footstep data. This floor system may be used to transparently identify users in their everyday living and working environments. We have created user footstep models based on footstep profile features and have been able to achieve a recognition rate of 93 % using this feature-based approach. We have also shown that the effect of footwear is negligible on recognition accuracy.

This paper reports ongoing experience with the design and everyday use of an electronic context-enabled in/out board. We designed this application as part of the development of a context toolkit and it proved a fruitful test-bed for investigating issues of context sensor fusion. We describe the first version of the application that used a single context sensor and explain some usability problems it raised. We analyze the limitations of available context sensors and conclude that the usability problems cannot be overcome using a single sensor. We suggest solutions relying on the use of multiple context sensors and sensor fusion. Keywords Context sensing, Sensor fusion, Ubiquitous computing INTRODUCTION Context sensing is recognized as an important feature for ubiquitous computing. By sensing context information such as the location and identity of people and objects, context-enabled applications can present context information to users, or modify their behavior according to changes ...