We present design and implementation of a chain of particles that can be programmed to fold the chain into a given curve. The particles guide an external force to fold, therefore the particles are simple and amenable for miniaturization. A chain can consist of a large number of such particles. Using multiple of these chains, a shape-shifting display can be constructed that folds its initially flat surface to approximate a given 3D shape that can be touched and modified by users, for example, enabling architects to interactively view, touch, and modify a 3D model of a building.

Long-term accurate measurements of core body temperature are essential to study human thermoregulation in ambulatory settings and during exercise, but they are traditionally carried out using highly-invasive techniques. To enable a continuous unobtrusive monitoring of core body temperature on ambulatory patients and exercising athletes, we have designed a wireless wearable system that measures the tympanic temperature inside the ear, as well as skin and environmental temperature, and that allows remote monitoring of the collected measurements. In this paper, we describe the design and implementation of the system and show that it can be used to identify the circadian rhythms of core body temperature, as well as to detect the variation in core body temperature due to prolonged physical exertion. We further describe the lessons learnt during a pilot deployment of our telemetric system on several athletes during the 5thLübeck Marathon, and discuss the impact of environmental parameters such as temperature and wind on the accuracy and meaningfulness of the measured values.

Pervasive services may have to rely on multimodal classification to implement situation-recognition. However, the effectiveness of current multimodal classifiers is often not satisfactory. In this paper, we describe a novel approach to multimodal classification based on integrating a vision sensor with a commonsense knowledge base. Specifically, our approach is based on extracting the individual objects perceived by a camera and classifying them individually with non-parametric algorithms; then, using a commonsense knowledge base, classifying the overall scene with high effectiveness. Such classification results can then be fused together with other sensors, again on a commonsense basis, for both improving classification accuracy and dealing with missing labels. Experimental results are presented to assess, under different configurations, the effectiveness of our vision sensor and its integration with other kinds of sensors, proving that the approach is effective and able to correctly recognize a number of situations in open-ended environments.