First responders engage in highly stressful situations at the emergency site that may induce stress, fear, panic and a collapse of clear thinking. Staying cognitively under control under these circumstances is a necessary condition to avoid useless risk-taking and particularly to provide accurate situation reports to organize appropriate support in time. This work-in-progress applied a flexible virtual reality (VR) training environment to investigate the performance of reporting under rather realistically simulated mission conditions. In a pilot study, representative emergency forces of the Austrian volunteer fire brigade and paramedics of the Johanniter organization participated in an exploratory pilot study that tested a formalized reporting schema (LEDVV), applying equivalent stress in both, (i) real (physical strain) and non-immersive (cognitive strain), and (ii) fully immersive training environments. Wearable psychophysiological measuring technology was applied to estimate the cognitive-emotional stress level under both training conditions. The results indicate that situation reports achieve a high level of cognitive-emotional stress and should be thoroughly trained. Furthermore, the results motivate the use of VR environments for the training of stress-resilient decision-making behavior of emergency forces.

Military organizations have extensive technological solutions to precisely monitor machines and operating equipment. In recent decades, extensive research and development projects have been launched focusing on the physiological monitoring of soldiers, with new opportunities arising from innovative developments in the field of biosensors. This paper describes the main objectives of the VitalMonitor project, which is carried out in the frame of the Austrian Defence Research Program FORTE (FORTE - Austrian Defence Research Program; https://projekte.ffg.at/projekt/3781447 ). The project focuses on the development of a real-time monitoring system for situation-dependent physiological load on soldiers based on innovative body worn biosensors integrated into clothing or equipment. Intelligent sensor fusion and data analysis methods enable an overview of the actual physical stress situation in military training, exercises or missions. The analysis of scenario-based physiological requirements will be the basis for the optimization of physical resilience as well as operational readiness and will eventually reduce the risk for dangerous situations caused by physical exhaustion.

We present a demonstration system for multimodal interaction with production machinery. We implemented mixed reality, gestural, and speech interactions with OPC UA connections for controlling injection molding machinery. This way we provide the user a single interaction system for controlling a multitude of production machines, enabling easier, faster, and less-demanding interactions. The system is fully implemented with a working machine and is used as a showcase demonstrator.

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).

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.

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.