Beside their stunning graphics, modern entertain-compensate for various inaccuracies inherent in inexpensive ment systems feature ever-higher levels of immersive user-wearable sensor system. The inaccuracies result from the interaction. Today, this is mostly achieved by virtual (VR) and limited resolution and sampling rate of the sensors, variations augmented reality (AR) setups. On top of these, we envision to add ambient intelligence and context awareness to gaming in sensor placement, dynamic sensor displacement during applications in general and games of martial arts in particular. user motion, and other sources of noise (e.g., environmental To this end, we conducted an initial experiment with inexpensive magnetic fields or temperature-related sensor drift). The body-worn gyroscopes and acceleration sensors for the Chum challenge in algorithmic design is to find features that are Kiu motion sequence in Wing Tsun (a popular form of Kung sensitive to the relevant motion characteristics and at the Fu). The resulting data confirm the feasibility of our vision.. .s Fine-tuned adaptations of various thresholding and pattern-same time insensitive to the inaccuracies mentioned before. matching techniques known from the fields of computational As decribed in Section III, we conducted an initial experintelligence and signal processing should suffice to automate the iment capturing Wing Tsun movements with wearable gyroanalysis and recognition of important Wing Tsun movements scopes and acceleration sensors to test the feasibility of our in real time. Moreover, the data also seem to allow for the vision. Section IV discusses and analyzes the experimental possibility of automatically distinguishing between certain levels vision. Section IV discussesand ana es the erieon of expertise and quality in executing the movements.results, provilng to be very promsilng indeed. Based thereon, Keywords: Body-worn Sensors, Experiment, Games of it certainly seems worthwhile to continue in this direction Martial Arts, Kung Fu, Motion Analysis, Movement Recog-and try to automate at least some parts (if not all) of an nition, Wearable Computing, Wing Tsun expert analysis for many important Wing Tsun movements. Beside in sports and game play, martial arts from the I. INTRODUCTION Far East gain ever more popularity and importance in many Video analysis and motion capturing are standard tools in other areas as well. Tai Chi, for instance, is of special professional sports to monitor and improve athletic perfor-interest because clinical studies show that it helps to reduce mance by recognizing and fine-tuning the quality of move-the probability of falling, especially for the elderly [l] and ment. Cutting-edge systems with high-quality sensors hardly patients with chronic conditions [2]. suffice to fulfill these professionals' needs. Quite often, trainers and other experts still process the recorded data by II. RELATED WORK hand. The whole setup and procedure are not only expensive By now, many independent researchers have demonstrated and...
Abstract. The standardization of the design of learning games is a contradictory topic: The existence of a rich variety of domains and applications is in conflict with the desire for unification that would result in improved reusability, interoperability and reduction of design complexity. In this paper, we describe the use of the ICOPER Reference Model (IRM) specification as foundation layer for the design of digital learning games. This reference model incorporates design and development processes as well as standards such as IMS Learning Design, a framework for presenting content according to logical rules like conditions and properties. The paper reports about exemplary learning games that make use of e-learning standards the IRM consists of, and explains about potential and limitations both from the game and e-learning design perspective, resulting in suggestions how to close missing links.
IMS Learning Design (IMS LD) is an open specification to support interoperability of advanced educational designs for a wide range of technology-enhanced learning solutions and other units of learning. This paper analyses approaches to model personalised learning experiences with and without explicit adaptive features in IMS LD. The paper has two main parts. The first part analyses the relation between orchestrating learning and IMS LD's semantic features. The second part compares modelling strategies for educational designs for personalised learning in non-collaborative learning units using IMS LD Level A and IMS LD Level B features. The analysis is based on two worked-out IMS LD units. The paper concludes with a comparison of the two modelling approaches and addresses gaps when integrating adaptation concepts at the levels of the specification.
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