This paper presents a low-cost and practical approach to achieve basic input using a tactile cube-shaped object, augmented with a set of sensors, processor, batteries and wireless communication. The algorithm we propose combines a finite state machine model incorporating prior knowledge about the symmetrical structure of the cube, with maximum likelihood estimation using multivariate Gaussians. The claim that the presented solution is cheap, fast and requires few resources, is demonstrated by implementation in a small-sized, microcontroller-driven hardware configuration with inexpensive sensors. We conclude with a few prototyped applications that aim at characterizing how the familiar and elementary shape of the cube allows it to be used as an interaction device.

A wearable system is described that enables users to perform simple command selection and input operations. The system is suitable for use while moving and in almost any posture. The implementation uses RFID and is low in cost and power requirements. There is provision for changing between application contexts, making the system suitable for use in mobile and ubiquitous computing environments where the mobility of the user requires frequent changes of focus. The paper includes a preliminary evaluation of the system's usability.

Abstract. Weight training, in addition to aerobic exercises, is an important component of a balanced exercise program. However, mechanisms for tracking free weight exercises have not yet been explored. In this paper, we study methods that automatically recognize what type of exercise you are doing and how many repetitions you have done so far. We incorporated a three-axis accelerometer into a workout glove to track hand movements and put another accelerometer on a user’s waist to track body posture. To recognize types of

We present results on the recognition of intentional human gestures for video annotation and retrieval. We define a gesture as a particular, repeatable, human movement having a predefined meaning. An obvious application of the work is in sports video annotation where umpire gestures indicate specific events. Our approach is to augment video with data obtained from accelerometers worn as wrist bands by one or more officials. We present the recognition performance using a Hidden Markov Model approach for gesture modeling with both isolated gestures and gestures segmented from a stream. 1.

In this paper we describe the design and architecture of an adaptive proactive environment in which information, which reflects the communal interests of current inhabitants, is proactively displayed on largescale public displays. Adaptation is achieved through implicit communication between the environment and personal sensor devices worn by users. These devices, called Pendle, serve two purposes: they store and make available to the environment user preferences, and they allow users to override the environment’s proactive behavior by means of simple gestures. The result is a smooth integration of environment-controlled interaction (experienced by the user as implicit interaction, triggered by their presence) and user-controlled explicit interaction. Initial results show that user-controlled adaptation leads to an engaging user experience that is unobtrusive and not distracting. 1.

We present in this paper a complete gestural interface built to support music pedagogy. The development of this prototype concerned both hardware and software components: a small wireless sensor interface including accelerometers and gyroscopes, and an analysis system enabling gesture following and recognition. A first set of experiments was conducted with teenagers in a music theory class. The preliminary results were encouraging concerning the suitability of these developments in music education.

Abstract. Signals from sensors are often analyzed in a sequence of steps, starting with the raw sensor data and eventually ending up with a classification or abstraction of these data. This paper will give a practical example of how the same information can be trained and used to initiate multiple interpretations of the same data on different, application-oriented levels. Crucially, the focus is on expanding embedded analysis software, rather than adding more powerful, but possibly resource-hungry, sensors. Our illustration of this approach involves a tangible input device the shape of a cube that relies exclusively on lowcost accelerometers. The cube supports calibration with user supervision, it can tell which of its sides is on top, give an estimate of its orientation relative to the user, and recognize basic gestures.

Abstract. Gesture is a natural expression form for humans, but its recognition is a similarly hard problem as speech recognition. In this paper, I present a real-time hand gesture recognition system, which identifies relevant parts in the continous sensor data stream, and classifies them to the most probable gesture. Instead of the usual button-based segmentation, I have created an automatic segmentation method, which makes the interface more natural. The results showed that the two different classifiers reach 97.4 % and 96 % accuracy on a personalized gesture set, and these results can be improved for certain gesture sets with the combination of the two algorithms. Furthermore, the system has great performance and low response time, so the user experience is much better than with previous gesture recognizers.

Most driver information systems offered in automobiles today display vehicle speed, fluid levels, fluid temperatures, and some basic diagnostic information (warnings, panel lamps). Optional driver information systems add to this list by offering fuel economy information, compass heading, outside temperature and other comfort and convenience related items. Very few provide information in regards to the real performance of the vehicle, its motion in 3-dimensional space, or the driver's skill and performance. Making this information available to the driver can enhance the "fun-to-drive" aspects of driving.

architecture, which contains a number of circuit boards (panes), currently used in an application for on-shoe gait analysis. Each pane instantiates a major function -- e.g. inertial sensing, tactile sensing or data collection and transmission. As opposed to similar architectures, this system treats the sensor panes as discrete design objects that have data collection as their primary goal. This architecture has allowed us to develop a shoe-mounted system that is capable of measuring gait parameters outside of a traditional motion laboratory. The small size of the circuit boards allows for a compact attachment that fits on the back of the shoe, and the integrated wireless transceiver allows the data to be collected continuously and in any environment.