There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.

/pdf/activity-recognition-from-user-annotated-acceleration-data-lh3b5ajkau.pdf

Activity recognition from user-annotated acceleration data

How we Think

The theory of affordances

Cultivating Communities of Practice: A Guide to Managing Knowledge

Providing sustainable energy is one of the fundamental challenges for mankind. With energy usage being a part of everyday activities and with the increasingly diversity of energy creation this is an inherently multidisciplinary problem. Transportation and travel, heating and cooling, manufacturing and production are major areas in which energy is used and all these domains become more and more linked to ubiquitous computing. With an increase in decentralized energy provision, ranging from energy harvesting in devices to personal green power plants, a great potential for creating sustainable energy arises, however at the cost of a higher complexity of the distribution network and storage mechanisms. Overall we believe that research in ubiquitous computing can provide important contributions for a world with sustainable energy. In this workshop we hope to get people from different disciplines together to share their visions and insights on how to conserve, efficiently produce, use, and distribute energy.

http://www.vs.inf.ethz.ch/publ/papers/Albrecht%20Schmidt-ubiqui-2010.pdf

Ubiquitous Computing for Sustainable Energy (UCSE2010) Ubicomp 2010 Workshop

Head movement is widely used as a uniform type of input for human-computer interaction. However, there are fundamental differences between head movements coupled with gaze in support of our visual system, and head movements performed as gestural expression. Both Head-Gaze and Head Gestures are of utility for interaction but differ in their affordances. To facilitate the treatment of Head-Gaze and Head Gestures as separate types of input, we developed HeadBoost as a novel classifier, achieving high accuracy in classifying gaze-driven versus gestural head movement (F1-Score: 0.89). We demonstrate the utility of the classifier with three applications: gestural input while avoiding unintentional input by Head-Gaze; target selection with Head-Gaze while avoiding Midas Touch by head gestures; and switching of cursor control between Head-Gaze for fast positioning and Head Gesture for refinement. The classification of Head-Gaze and Head Gesture allows for seamless head-based interaction while avoiding false activation.

Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input

Space-multiplexed input allows the distribution of tasks across spatially separated input devices. In this paper, we present an empirical study that evaluates the practical applicability of space-multiplexed input to mouse-extended notebooks - a common configuration which integrates both a touchpad and an external mouse. Our study shows that (1) two-handed input can instantly be performed by subjects without significant loss in performance; (2) one-handed operation of space-multiplexed interfaces performs worse than time-multiplexed input; and (3) the touchpad can be faster to acquire than virtual handles of standard size.

Space-multiplexed input on mouse-extended notebooks

Indoor location systems enable mobile devices to gather location information of other devices or services. Applications such as the Relate Meeting Tool [2] have been built that support cross-device interaction without explicitly knowing an address for the target device. However, little is known about how to integrate this spatial information into the user interface in an effective way. This poster introduces our approach and presents preliminary results.

/pdf/integrating-spatial-information-in-a-user-interface-5537v2sr42.pdf

Integrating spatial information in a user interface

As eye tracking in augmented and virtual reality (AR/VR) becomes established, it will be used by broader demographics, increasing the likelihood of tracking errors. Therefore, it is important when designing eye tracking applications or interaction techniques to test them at different signal quality levels to ensure they function for as many people as possible. We present GE-Simulator, a novel open-source Unity toolkit that allows the simulation of accuracy, precision, and data loss errors during real-time usage by adding gaze vector errors into the gaze vector from the head-mounted AR/VR eye tracker. The tool is customisable without having to change the source code and changes in eye tracking errors during and in-between usage. Our toolkit allows designers to prototype new applications at different levels of eye tracking in the early phases of design and can be used to evaluate techniques with users at varying signal quality levels.

Hans Gellersen

Papers

Ubiquitous Computing for Sustainable Energy (UCSE2010) Ubicomp 2010 Workshop

Classifying Head Movements to Separate Head-Gaze and Head Gestures as Distinct Modes of Input

Space-multiplexed input on mouse-extended notebooks

Integrating spatial information in a user interface

GE-Simulator: An Open-Source Tool for Simulating Real-Time Errors for HMD-based Eye Trackers