Daily appropriate decision making on nutrition requires application of knowledge where it matters, and being adjusted to the individual requirements. We present a highly personalized mobile application that assists the user in appropriate food choices during grocery shopping, while simultaneously incorporating a personalized dietary recommender system. The application can be used in video based augmented reality mode, where a computer vision algorithm recognizes presented food items and thus replaces tedious search within the food database. The recognition system employs a shallow Convolutional Neural Network (CNN) based classifier running at 10 fps. An innovative user study demonstrates the high usability and user experience of the application. The vision classifier is evaluated on a newly introduced reference image database containing 81 grocery foods (vegetables, fruits).

Im Rahmen des Projektes „4C4FirstResponder“ wird die Entwicklung einer Kommunikationsund Informationslösung zur optimierten Einsatzführung von Interventionskräften angestrebt. Dazu werden multisensorale Daten (Text, Audio, Bilder, Video, etc.) generiert, zu einem Lagebild zusammengeführt sowie rollenbasiert verteilt (an mobile Teams, Einsatzleitung, etc.). Kern der Kommunikationslösung ist ein Kommunikationsmanager, der TETRA und andere Netzwerktechnologien integriert, die Vorteile aller Technologien nutzen kann und Daten entsprechend von QoS-Anforderungen über diese verschiedenen Technologien verschickt. Eine weitere wichtige Komponente der Lösung ist eine Szenarien-orientierte Darstellung der Daten und Bedienung auf (mobilen) Endgeräten. Das Projekt verfolgt einen agilen Entwicklungsansatz und startete mit einer Bedarfserhebung bei den Endbenutzern (Einsatzkräfte). Damit konnte eine erste Identifikation von Einsatzszenarien und Anwendungsfällen erfolgen und ein Architekturentwurf des Systems und des Kommunikationsmanagers erstellt werden, welche in dieser Arbeit vorgestellt werden.

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is engaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment.

The prevention of cardiovascular diseases becomes more and more important, as malnutrition accompanies today's fast moving society. While most people know the importance of adequate nutrition, information on advantageous food is often not at hand, such as in daily activities. Decision making on individual dietary management is closely linked to the food shopping decision. Since food shopping often requires fast decision making, due to stressful and crowded situations, the user needs a meaningful assistance, with clear and rapidly available associations from food items to dietary recommendations. This paper presents first results of the Austrian project (MANGO) which develops mobile assistance for instant, situated information access via Augmented Reality (AR) functionality to support the user during everyday grocery shopping. Within a modern diet - the functional eating concept - the user is advised which fruits and vegetables to buy according to his individual profile. This specific oxidative stress profile is created through a short in-app survey. Using a built-in image recognition system, the application automatically classifies video captured food using machine learning and computer vision methodology, such as Random Forests classification and multiple color feature spaces. The user can decide to display additional nutrition information along with alternative proposals. We demonstrate, that the application is able to recognize food classes in real-time, under real world shopping conditions, and associates dietary recommendations using situated AR assistance.

Understanding human attention in mobile interaction is a relevant part of human computer interaction, indica-ting focus of task, emotion and communication. Lack of large scale studies enabling statistically significant re-sults is due to high costs of manual penetration in eye tracking analysis. With high quality wearable cameras for eye-tracking and Google glasses, video analysis for visual attention analysis will become ubiquitous for automated large scale annotation. We describe for the first time precise gaze estimation on mobile displays and surrounding, its performance and without markers. We demonstrate accurate POR (point of regard) re-covery on the mobile device and enable heat mapping of visual tasks. In a benchmark test we achieve a mean accuracy in the POR localization on the display by 1.5 mm, and the method is very robust to illumination changes. We conclude from these results that this sys-tem may open new avenues in eye tracking research for behavior analysis in mobile applications.

My Places Diary is a research prototype, available on Google Play, that tracks places and movements of users throughout the day. It includes two innovative features: Firstly users' home and workplaces are detected automatically, based on heuristics derived from earlier work on automatic semantic place detection. Additional place labels are provided via open geo data sources. The position is determined by using a low power positioning method while preserving sufficient accuracy for place detection. Secondly modes of transportation between places (walking, driving, biking) are derived based on Google's activity recognition module. Combining these two features makes My Places Diary an automatically created diary of places and transitions between them. All computations are executed directly on the mobile phone.

Analysis of human geographical orientation is a crucial issue to understand the user's actual demand on context based information. As the limitations in accuracy of satellite based positioning especially in urban environments and network based positioning are well known, a novel framework concept based on data fusion of multiple sensors build-in handsets will enable to characterize the user's situation and allow automated analysis with semantic mapping functionality. In this paper, the first approach for high accuracy multi sensor orientation tracking by the use of a mobile phone is discussed.

We present a study investigating two novel mobile services supporting querying for information in the urban environment using camera equipped smart phones as well as two different ways to visualize results -- icon-based visualization and text-based visualization. Both applications enable the user to access information about an object by snapping a photo of it. We investigate how users would use a photo-based tourist guide in a free exploration setting in general as well as the acceptance/preference of two different ways to visualize results.

We present a computer vision system for the detection and identification of urban objects from mobile phone imagery, e.g., for the application of tourist information services. Recognition is based on MAP decision making over weak object hypotheses from local descriptor responses in the mobile imagery. We present an improvement over the standard SIFT key detector (Lowe, 2004) by selecting only informative (i-SIFT) keys for descriptor matching. Selection is applied first to reduce the complexity of the object model and second to accelerate detection by selective filtering. We present results on the MPG-20 mobile phone imagery with severe illumination, scale and viewpoint changes in the images, performing with p ap 98% accuracy in identification, efficient (100%) background rejection, efficient (0%) false alarm rate, and reliable quality of service under extreme illumination conditions, significantly improving standard SIFT based recognition in every sense, providing mportant for mobile vision - runtimes which are ap 8 (ap 24) times faster for the MPG-20 (ZuBuD) database

We describe a system which proposes a solution for multi-sensor object awareness and positioning to enable stable location awareness for a mobile service in urban areas. The system offers technology of outdoors vision based object recognition that will extend state-of-the-art location and context aware services towards object based awareness in urban environments. In the proposed application scenario, tourist pedestrians are equipped with a GPRS or UMTS capable camera-phone. They are interested whether their field of view contains tourist sights that would point to more detailed information. Multimedia type data about related history might be explored by a mobile user who is intending to learn within the urban environment. Ambient learning is in this way achieved by pointing the device towards an urban sight, capturing an image, and consequently getting information about the object on site and within the focus of attention, i.e., the user's current field of view. The described mobile system offers multiple opportunities for application in both mobile business and commerce, and is currently developed as an industrial prototype.

Handheld mobile applications will become a driving economic factor for the developments in telecommunication industry and also for the acceptance and public utilisation of the EU initiative Galileo. Tourism information mostly is geographically related information and offers interesting location aware applications. This paper will briefly describe a prototype of a mobile multimedia tourism information system for outdoor activities. A PDA (Personal Digital Assistant) in connection with GPS function allows to guide tourists to the desired sights or to hiking and biking tours of a tourism region. For a multimedia visualisation of tourism information pictures, sound, text and video elements were integrated into the PDA in order to record the effectiveness of the mobile device. Cartographic illustrations using satellite images and digital maps of the tourism site and its surroundings provide further information. Therefore, the mobile device offers the possibility of an innovative presentation of tourism information and fulfils user desires to information provision.