The Passive Leg Raise (PLR) test serves as an essential diagnostic instrument for evaluating fluid responsiveness within intensive care units (ICUs). Nevertheless, its execution and interpretation are susceptible to errors resulting from procedural complexity and clinician workload. We introduce A-PLR, an activity-aware assistant integrated into a smart ICU setting to mitigate these challenges. The system employs computer vision and spatial activity recognition to autonomously identify PLR phases, ensure procedural compliance, and provide real-time decision support via an intuitive bedside interface. This paper reports a mixed-method evaluation of A-PLR, comprising both quantitative performance metrics and qualitative insights from clinical professionals (N = 30). Within the controlled experimental setting, the results indicate improved usability and procedural support, as well as indications of enhanced diagnostic assistance and reduced cognitive load when clinicians were supported by A-PLR. In addition, user feedback highlights the potential of activity-aware, AI-driven systems to support decision-making and streamline workflow integration in (simulated) critical care environments.

To fulfill the promises of Augmented Reality (AR) in the manufacturing and maintenance industry, the development of AR application has to become faster, simpler and cheaper. Since typical AR applications make use of basic functionality like reading markers and displaying simple overlays, a clever combination of such basic building blocks would ease the development of complex AR applications. In this paper, we present a distributed architecture for creating and managing basic AR services, e.g. for information management, user interaction, and documentation. This way, complex AR processes can be orchestrated from those services for being used in different application domains.

We present an interactive and multimodal-based augmented reality system for computer-assisted surgery in the context of ear, nose and throat (ENT) treatment. The proposed processing pipeline uses fully digital stereoscopic imaging devices, which support multispectral and white light imaging to generate high resolution image data, and consists of five modules. Input/output data handling, a hybrid multimodal image analysis and a bi-directional interactive augmented reality (AR) and mixed reality (MR) interface for local and remote surgical assistance are of high relevance for the complete framework. The hybrid multimodal 3D scene analysis module uses different wavelengths to classify tissue structures and combines this spectral data with metric 3D information. Additionally, we propose a zoom-independent intraoperative tool for virtual ossicular prosthesis insertion (e.g. stapedectomy) guaranteeing very high metric accuracy in sub-millimeter range (1/10 mm). A bi-directional interactive AR/MR communication module guarantees low latency, while consisting surgical information and avoiding informational overload. Display agnostic AR/MR visualization can show our analyzed data synchronized inside the digital binocular, the 3D display or any connected head-mounted-display (HMD). In addition, the analyzed data can be enriched with annotations by involving external clinical experts using AR/MR and furthermore an accurate registration of preoperative data. The benefits of such a collaborative surgical system are manifold and will lead to a highly improved patient outcome through an easier tissue classification and reduced surgery risk.

With the ubiquity of technology and tools, current Volunteered Geographic Information (VGI) projects allow the public to contribute, maintain, and use geo-spatial data. One of the most prominent and successful VGI project is OpenStreetMap (OSM), where more than one million volunteers collected and contributed data that is obtainable for everybody. However, this kind of contribution mechanism is usually associated with data quality issues, e.g., geographic entities such as gardens or parks can be assigned with inappropriate classification by volunteers. Based on the observation that geographic features usually inherit certain properties and characteristics, we propose a novel classification-based approach allowing the identification of entities with inappropriate classification. We use the rich data set of OSM to analyze the properties of geographic entities with respect to their implicit characteristics in order to develop classifiers based on them. Our developed classifiers show high detection accuracies. However, due to the absence of proper training data we additionally performed a user study to verify our findings by means of intra-user-agreement. The results of our study support the detections of our classifiers and show that our classification-based approaches can be a valuable tool for managing and improving VGI data.

Small and large-scale disasters are a major factor for poverty. When information is sent out at an early stage and directly to people affected, impact on environment, people, livestock, crop, and belongings can be minimized. We present Mobile4D, an integrated mobile crowdsourcing-based disaster alerting and reporting system tested in Lao PDR. With Mobile4D it is possible to gather information from affected people, to establish direct communication channels between affected people and administrative units, and to rapidly distribute information to regions and people struck by disasters.

In this paper we introduce a way of tracking people in an indoor environment across multiple cameras with overlapping as well as non-overlapping fields of view. To do so, we use our distribution model called SpARTA and an extended Tracking-Learning-Detection algorithm. A big advantage in comparison to other systems is that each camera node learns the tracked person and builds a database of positive and negative examples in real time. With these datasets we are able to distinguish different people across different nodes. The learned data is shared across nodes, so that they improve each other while tracking. In the main part we present an experimental validation of the system. Finally, we will show that distribution of tracking data improves tracking across multiple nodes considerably with regard to partial occlusion of the tracked object.

This paper presents a novel solution to the focus-and-context problem of mobile maps provided for local and global orientation. Our solution is inspired by the design principles of static You-Are-Here maps and realizes principles of human spatial cognition to enable efficient communication of location information. We further propose selective interaction with the presented information to improve the speed and accuracy of interpretation of the geographic information. Tests show strong evidence for the cognitive and interaction efficiency of the resulting maps, as users were faster and more accurate than with conventional mobile maps.