Millions of people run. Movement scientists investigate the relationship of running kinematics to fatigue, injury, or running economy mainly using optical motion capture. It was found that running kinematics are highly individual and often cannot be summarized by single variables. We thus present a data-driven analysis of running technique using wearable technology, combining statistical features and machine learning techniques, which allows to identify non-linear, complex relationships. Wearable technology enables running kinematic analysis to a broad mass in unconstrained environments. 20 runners wore 12 sensor units during two experiments: an all out test and a fatiguing run. We used a Support Vector Machine (SVM) to distinguish skill level groups and achieved an accuracy of 76.92% with an acceleration sensor on the upper body. Sensor positions were ranked according to the movement change with fatigue using a feature selection. This ranking was consistent with visual annotations of a movement scientist. We propose a quantitative measure of movement change using a principal component analysis (PCA) and found an average correlation of 0.8369 for all runners with their perceived rating of fatigue.

In large-scale, complex domains such as space defense and security systems, situation assessment and decision making are evolving from centralized models to high-level, net-centric models. In this context, collaboration among the many actors involved in the situation assessment process is critical to achieve a prompt reaction as needed in the operational scenario. In this paper, we propose a multiagent-based approach to situation assessment, where agents cooperate by sharing local information to reach a common and coherent assessment of situations. Specifically, we characterize situation assessment as a classification process based on OWL ontology reasoning, and we provide a protocol for cooperative multiagent situation assessment, which allows the agents to achieve coherent high-level conclusions. We validate our approach in a real maritime surveillance scenario, where our prototype system effectively supports the user in detecting and classifying potential threats; moreover, our distributed solution performs comparably to a centralized method, while preserving independence of decision makers and dramatically reducing the amount of communication required. © 2012 Wiley Periodicals, Inc.

This paper describes the Ready-to-Live project carried out at the ETH Zurich, Electronics Laboratory in collaboration with Swiss Textile College (STF) during the 2010 spring semester. The goal of the project was to provide an interdisciplinary collaboration for technical and fashion students, and to present the final result in a real fashion show. In this paper, we show how we integrated wearable sensors into fashionable clothes in order to express different feelings and emotions. We explain first the technical implementation and then we present the design aspects of the final outfits.

We present an unsupervised speaker identification system for personal annotations of conversations and meetings. The system dynamically learns new speakers and recognizes already known speakers using one audio channel and speech-independent modeling. Multiple personal systems could collaborate in robust unsupervised speaker identification and online learning. The system was optimized for real-time operation on a DSP system that can be worn during daily activities. The system was evaluated on the freely available 24-speaker Augmented Multiparty Interaction dataset. For 5 s recognition time, the system achieves 81% recognition rate. Collaboration between four identification systems resulted in a performance increase of up to 17%, however even two collaborating systems yield an performance improvement. A prototypical wearable DSP implementation could continuously operate for more than 8 hours from a 4.1 Ah battery.