On 01.10.2021, the research project GreifbAR started under the leadership of the DFKI (research area Augmented Reality). The goal of the GreifbAR project is to make mixed reality (MR) worlds, including virtual (VR) and augmented reality (“AR”), tangible and graspable by allowing users to interact with real and virtual objects with their bare hands. Hand accuracy and dexterity is paramount for performing precise tasks in many fields, but the capture of hand-object interaction in current MR systems is woefully inadequate. Current systems rely on hand-held controllers or capture devices that are limited to hand gestures without contact with real objects. GreifbAR solves this limitation by introducing a sensing system that detects both the full hand grip including hand surface and object pose when users interact with real objects or tools. This sensing system will be integrated into a mixed reality training simulator that will be demonstrated in two relevant use cases: industrial assembly and surgical skills training. The usability and applicability as well as the added value for training situations will be thoroughly analysed through user studies.

Partners

Berliner Charite (University Medicine Berlin) NMY (Mixed reality applications for industrial and communication customers) Uni Passau (Chair of Psychology with a focus on human-machine interaction).

Artificial Intelligence (AI) has a major impact on many sectors and its influence is predicted to expand rapidly in the coming years. One area with considerable untapped potential for AI is the field of Earth Observation, where it can be used to manage large datasets, find new insights in data and generate new products and services. AI is one of the missing core areas that need to be integrated in the EO capabilities of the ERATOSTHENES Centre of Excellence (ECoE). AI-OBSERVER project aims to significantly strengthen and stimulate the scientific excellence and innovation capacity, as well as the research management and administrative skills of the ECoE, through several capacity building activities on AI for EO applications in the Disaster Risk Reduction thematic area, upgrading and modernising its existing department of Resilient Society, as well as its research management and administration departments, and assisting the ECoE to reach its long-term objective of raised excellence on AI for EO on environmental hazards. A close and strategic partnership between the ECoE from Cyprus (Widening country) and two internationally top-class leading research institutions, the German Research Centre for Artificial Intelligence (DFKI) from Germany and the University of Rome Tor Vergata (UNITOV) from Italy, will lead to a research exploratory project on the application of AI on EO for multi-hazard monitoring and assessment in Cyprus. Moreover, CELLOCK Ltd. (CLK), the project’s industrial partner, will lead commercialisation, exploitation and product development aspects of AI-OBSERVER and its exploratory project outputs. All outputs will be disseminated and communicated to stakeholders, the research community, and the public, assisting the ECoE to accomplish its exploitation goals, by creating strong links with various stakeholders from academia and industry in Cyprus and beyond, that ECoE will capitalise on, long after the end of the project.

Partners

ERATOSTHENES Centre of Excellence (ECoE), Zypern (Koordinator) DFKI, Deutshland Universität Rom Tor Vergata, Italien CELLOCK Ltd., Zypern

Motivation

Artificial intelligence currently influences many areas, including machine vision. For applications in the fields of autonomous systems, medicine and industry there are fundamental challenges: 1) generating prior knowledge to solve severely under-determined problems, 2) verifying and explaining the response calculated by the AI, and 3) providing AIs in scenarios with limited computing power.

Goals and Procedure

The goal of VIDETE is to use AI to generate prior knowledge using machine learning processes, thus making previously unsolvable tasks such as the reconstruction of dynamic objects practically manageable with just one camera. With suitable prior knowledge it will be easier to analyze and interpret general scenes, for example in the area of autonomous systems, with the help of algorithms. Furthermore, methods will be developed to justify the calculated results before they are used further. In the field of medicine this would be comparable to the opinion of a colleague in contrast to the general answers of current AI methods. A key technique is considered to be the modularization of algorithms, which will especially increase the availability of AI. Modular components can be realized efficiently in hardware. Thus, calculations (e.g. the recognition of a gesture) can be performed close to the generating sensor. This, in turn, enables semantically enriched information to be communicated with low overhead, which means that AI can also be used on mobile devices with low resources available.

Innovations and Perspectives

Artificial intelligence finds its way into almost all areas of daily life and work. The results expected from the VIDETE project will be independent of the defined research scenarios and can contribute to progress in many application areas (private life, industry, medicine, autonomous systems, etc.).

Continuous bladder irrigation is the standard after operations on the bladder, prostate, or kidneys to prevent complications caused by blood clots. The irrigation should be monitored constantly, but this is not possible in everyday clinical practice. Therefore, the motivation of VisIMon is to enable automated monitoring. It leads to improved patient care while at the same time relieving the strain on staff.

The aim of the project VisIMon is the development of a small module worn on the body which monitors the irrigation process with the aid of various sensors. The system should seamlessly integrate with the established standard process. Through the cooperation of interdisciplinary partners from industry and research, the necessary sensors are to be developed and combined into an effective monitoring system. Modern communication technology enables the development of completely new concepts how medical devices interact with a hospital. At more than 200,000 applications per year in Germany, the development is extremely attractive not only from a medical but also from an economic point of view.

Partners

• Albert-Ludwigs-Universität Freiburg
• Fraunhofer Gesellschaft zur Förderung der Angewandten Forschung E.V.
• Lohmann & Birkner Health Care Consulting GmbH
• Digital Biomedical Imaging Systems AG

The research project LiSA is a broad-based joint project in the area of facade, lighting and control technology. The aim is enabling the most energy-efficient operation of office and administrative buildings taking into account user satisfaction. Through exemplary system integration the understanding of the interaction of individual components and the necessity of system solutions are promoted.

At component level technologies are being developed which enable the efficient use of daylight, provide energy-saving lighting with artificial light, and reduce cooling load during summer by means of shading. At the level of sensor technology, a cost-effective sensor is developed, which measures the light conditions as well as heat inputs to the room by solar radiation. A model-predictive control approach optimizes the operation of the components, which can be managed and controlled via wireless communication paths.

With the implementation of the project in a Living Lab Smart Office Space, which is subject to detailed monitoring and in which people use the space according to the actual purpose, it is ensured that the developments are continuously empirically validated and that the results are perceived by users as added value. The people working in the Living Lab have the opportunity to interact with the technology and are thus an essential part of the investigations.

Partners

• Technische Universität Kaiserslautern
• DFKI GmbH
• ebök Planung und Entwicklung GmbH
• Dresden Elektronik Ingenieurtechnik GmbH
• Agentilo GmbH
• Herbert Waldmann GmbH & Co. KG

The virgin stone marble has been used as preferred material for representative buildings and sculptures. Yet, due to its chemical composition and its porosity marble is prone to natural deterioration in outdoor environments, with an accelerating rate since the beginning of industrialization, mainly due to increasing pollution. A basic requirement for a successful restoration and conservation is a regularly repeated assessment of the current object condition and knowledge about prior restoration actions. Ideally the assessment is non-destructive. This requirement is fulfilled for both the optical digitization of objects shape and appearance, and the ultrasound examination used to acquire properties with respect to material quality.

Goal of the joint research project Marmorbild of the University Kaiserslautern, the Fraunhofer Institute (IBMT), and the Georg-August-University Göttingen is the validation of modern ultrasound technologies and digital reconstruction methods with respect to non-destructive testing of facades, constructions and sculptures built from marble. The proof of concept has been provided with prior research.

The planned portable assessment system holds a high potential for innovation. In the future, more objects can be examined cost-effectively in short time periods. Damage can be identified at an early stage allowing for a target-oriented investment of efforts and financial resources.

Partners

Funding by: BMBF

• Funding programm: VIP+
• Grant agreement no.: 03VP00293
• Begin: 01.10.2016
• End: 30.09.2019

A Framework for the Automatic Inspection of TRUS Images. The project ANNA (Artificial Neural Network Analyzer) aims at the design of a framework to analyze ultrasound images by means of signal processing combined with methods from the field of artificial intelligence (neural networks, self-organizing maps, etc.). Although not obvious ultrasound images do contain information that cannot be recognized by the human visual system and that do provide information about the underlying tissue. On the other hand the human visual system recognizes virtual structures in ultrasound images that are not related at all to the underlying tissue. Especially interesting in this regard is the fact that the careful combination several texture descriptor based filters is suited for an analysis by artificial neural networks and that suspicious regions can be marked reliably. The specific aim of the framework is to automatically analyze conventional rectal ultrasound (TRUS) images of the prostate in order to detect suspicious regions that are likely to relate to a primary cancer focus. These regions are marked for a subsequent biopsy procedure. The advantages of such an analysis are the significantly reduced number of biopsies compared to a random or systematic biopsy procedure to detect a primary cancer and the significantly enhanced success rate to extract primary cancer tissue with a single biopsy procedure. On one hand this results in a faster and more reliable diagnosis with significantly decreased intra-examiner variability, on the other hand the discomfort of the patient due to multiple biopsy sessions is dramatically reduced.

Contact

Dr. Gerd Reis

• gerd.reis@dfki.de