Funding source: andere Förderorganisation
Project leader: ,
David B. Blumenthal
Biomedical Network Science
Funding source: EU / European Research Council
Project leader:
Alessandro Del Vecchio
Neuromuscular Physiology and Neural Interfacing
We have recently identified single motor neuron activity in humans with spinal cord injury and stroke resulting in complete loss of hand function. We have then demonstrated that these individuals can control with high levels of precision the spared motor neurons ensemble in real-time, up to four degrees of freedom of the hand. We have now collected pilot data in children (
Funding source: BMBF / Verbundprojekt
Project leader:
Claudio Castellini
Assistive Intelligent Robotics
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
Project leader:
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader:
Andreas Kist
Artificial Intelligence in Communication Disorders
Project leader:
The aim of the Centre is to implement interdisciplinary projects related to the field of AI in medicine. This includes conducting and publishing scientific research and, where possible, translating it into practical applications. As a collaboration with the Universitätsklinikum Erlangen, scientific projects can be submitted by all interested colleagues.
Funding source: EU / European Research Council
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Project leader: ,
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Project leader:
Bernhard Kainz
Image Data Exploration and Analysis
Deep learning has emerged as a key technology in biomedical image analysis, but is difficult to handle for non-experts due to the high demands on computing power and data management. This project will develop a platform to facilitate large-scale statistical analysis of multimodal biomedical imaging and patient data using state-of-the-art deep learning methods. The proposed infrastructure will provide access to state-of-the-art algorithms and define a standardized data infrastructure that can be easily deployed in heterogeneous environments. Our prototype will provide an effective mechanism for sharing pre-trained algorithms and advanced analytical tools. The platform is aimed at the biomedical research community and will provide scientists with novel, powerful and validated tools to address challenges such as image-based disease phenotyping and predictive modeling. State-of-the-art analysis pipelines will be implemented and packaged into user-friendly toolboxes that can be directly used in clinical workflows and enable the extraction of imaging biomarkers and quantitative measurements. Our approach is based on three basic principles: Data linkage (across systems), data stewardship (patient privacy and legal/ethical compliance) and data interoperability (use of public APls and open standards). To achieve this, we will build on an existing model: Data will be kept in a secure environment, using AI algorithms to train with sensitive patient data within our clinic's firewall. Two approaches will be supported: a secure learning orchestration server that will provide learning coordination for secure data enclaves at our partner hospital, the University Hospital Erlangen (UKER), and secure sandboxes that will enable model development in a secure environment hosted by the university at FAU. As in a federated learning paradigm, most of the models will move through our infrastructure, not the data. We will set up (and support) the infrastructure in these environments with support from the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at FAU, the Radiology Department at UKER and the Regional Computing Center Erlangen (RRZE) to provide the required capabilities. Our proposed solution is highly interoperable and scalable for other clinics and enables integration e.g. with the Medical Informatics Initiative. This approach will provide secure and compliant access to the clinics' PACS and electronic patient records, and enable reproducible research.
Funding source: andere Förderorganisation
Project leader: , ,
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Project leader:
Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Funding source: DFG-Einzelförderung / Emmy-Noether-Programm (EIN-ENP)
Zum Verständnis der motorkortikalen Muskelsteuerung ist eine präzise Identifizierung der Motoreinheiten erforderlich, die die Muskelkraft steuern. Die motorische Einheit besteht aus einem einzelnen spinalen Motoneuron und einer Gruppe von innervierten Muskelfasern. Die Bewegung wird durch Aktionspotentiale erzeugt, die vom Gehirn, dem Rückenmark und afferenten Eingängen ausgehen. Sie Bewirken ein Aktionspotential auf der Ebene der spinalen Motoneuronen. Die Aufzeichnung der Aktivität der moto…
Funding source: EU - 9. Rahmenprogramm - Horizon Europe
Project leader: ,
In dAIbetes, we employ federated learning to develop a global health data platform that enables the creation of internationally trained virtual twin models for type 2 diabetes. Our models draw on large datasets from diverse sources while maintaining strict privacy standards. This groundbreaking method aims to enhance treatment outcome predictions, an area currently without precise guidelines, by using data from approximately 800,000 patients worldwide.
Our objective is to improve prediction accuracy by at least 10% compared to standard models, advancing personalized care for diabetes and other complex diseases. Our team combines expertise in AI, software development, privacy, and diabetes treatment, focusing on the essential balance between safeguarding data privacy and meeting medical research needs.
Funding source: BMBF / Verbundprojekt
Project leader: ,
David B. Blumenthal
Biomedical Network Science
In FLabNet, we will harness the potential of algorithmic network biology and distributed machine learning to address two exemplary unmet needs in paediatric oncology: prediction ofchemotherapy side effects like neutropenic fever and early-stage detection of rare malignantdiseases such as myeloproliferative neoplasms. Based on >54 million laboratory test resultsfrom >500,000 patients from the Core Dataset of the German Medical Informatics Initiative (MII),we will create personalised…
Funding source: andere Förderorganisation
Project leader:
David B. Blumenthal
Biomedical Network Science
Funding source: Bayerische Forschungsallianz (BayFOR)
Project leader:
Project leader: , , ,
Cell trafficking is crucially involved in the pathogenesis of immune-mediated inflammatory diseases such as rheumatoid arthritis or inflammatory bowel disease. While the contribution of cell surface receptors to such trafficking has been explored in detail and has already lead to therapeutic applications, cell-intrinsic properties affecting the cellular migratory behavior have largely been overlooked. Here, we hypothesize that cell mechanical properties and cell trafficking are inextricably linked.…
Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Project leader:
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Project leader:
Andreas Kist
Artificial Intelligence in Communication Disorders
Funding source: DFG / Klinische Forschungsgruppe (KFO)
Project leader: ,
Entzündung beginnt vor allem in Geweben, und ihre Ausprägung und ihr Ausmaß werden weitgehend von diesen kontrolliert. Infiltrierende Immunzellen sind hochgradig plastisch und integrieren sowohl molekulare als auch biophysikalische Informationen aus ihrer Umgebung, die allesamt ihre Effektor-Funktionen sowie ihr weiteres Schicksal bestimmen. Mit dem Fortschritt hochdimensionaler OMICs-Ansätze haben wir begonnen, die zelluläre Heterogenität komplexer entzündlicher Infiltrate besser zu verstehen. Währen…
Funding source: Industrie
Project leader:
David B. Blumenthal
Biomedical Network Science
Funding source: DFG / Forschungsgruppe (FOR)
Project leader:
Florian Knoll
Computational Imaging
Funding source: Industrie
Project leader:
David B. Blumenthal
Biomedical Network Science
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
Project leader:
The goal of this project is the development of an application for supporting the physical rehabilitation therapy of prostatectomy and incontinence patients in planning and execution. An AI-driven algorithm for automatic planning will be developed and extended by a machine learning approach for live exercise execution feedback. The developed application will be clinically evaluated regarding effectiveness and therapy benefit.
Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Project leader:
David B. Blumenthal
Biomedical Network Science
Rapid advances in single-cell RNA sequencing (scRNA-seq) technology are leading to ever-increasing dimensions of the generated molecular data, which complicates data analyses. In NetMap, new scalable and robust dimensionality reduction approaches for scRNA-seq data will be developed. To this end, dimensionality reduction will be integrated into a central task of the systems medicine analysis of scRNA-seq data: inference of gene regulatory networks (GRNs) and driver transcription factors…
Funding source: Deutsche Forschungsgemeinschaft (DFG)
Project leader:
David B. Blumenthal
Biomedical Network Science
The Cooperative Health Research in South Tyrol (CHRIS) study offers a comprehensive overview of the health state of >13,000 adults in the middle and upper Val Venosta. It is the largest population-based molecular study in Italy with a longitudinal lookout to investigate the genetic and molecular basis of age-related common chronic conditions and their interaction with lifestyle and environment in the general population. In CHRIS, the combination of molecular profiling data, such as genomics and…
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader: ,
Bernhard Kainz
Image Data Exploration and Analysis
Florian Knoll
Computational Imaging
The majority of diagnostic medical
imaging pipelines follow the same principles: raw measurement data is acquired
by scanner hardware, processed by image reconstruction algorithms, and then
evaluated for pathology by human radiology experts. Under this paradigm, every step
has traditionally been optimized to generate images that are visually pleasing
and easy to interpret for human experts. However, raw sensor information that
could maximize patient-specific diagnostic information may get lost in this
process. This problem is amplified by recent developments in machine
learning for medical imaging. Machine learning has been used successfully in
all steps of the diagnostic imaging pipeline: from the design of data
acquisition to image reconstruction, to computer-aided diagnosis. So far, these
developments have been disjointed from each other. In this project, we will
fuse machine learning for image reconstruction and for image-based disease
localization, thus providing an end-to-end learnable image reconstruction and
joint pathology detection approach that operates directly on raw measurement
data. Our hypothesis is that this combination can maximize diagnostic accuracy
while providing optimal images for both human experts and diagnostic machine
learning models.
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
Project leader: , ,
The goal of this project is the development of an application for supporting physical rehabilitation therapy in planning and execution. An AI-driven algorithm for automatic planning will be developed and extended by a machine learning approach for live exercise execution feedback. The developed application will be clinically evaluated regarding effectiveness and therapy benefit.
Funding source: DFG / Sonderforschungsbereich (SFB)
Project leader: ,
Andreas Maier
Department of Computer Science
X02 nutzt die in EBM erzeugten Bilddaten und mechanischen Messungen, um Deep Learning-Methoden zu entwickeln, die Wissen über Spezies hinweg transferieren. In silico und in vitro Analysen werden deutlich spezifischere Daten liefern als in vivo Experimente, insbesondere für menschliches Gewebe. Um hier Erkenntnisse aus datenreichen Experimenten zu nutzen, werden wir Transfer Learning-Algorithmen für heterogene Daten entwickeln. So kann maschinelles Lernen auch unter stark datenlimitierten Bedingungen nu…
Funding source: Europäische Union (EU)
Project leader:
Bernhard Kainz
Image Data Exploration and Analysis
As one of the most important aspects of diagnosis, treatment planning, treatment delivery, and follow-up, medical imaging provides an unmatched ability to identify disease with high accuracy. As a result of its success, referrals for imaging examinations have increased significantly. However, medical imaging depends on interpretation by highly specialised clinical experts and is thus rarely available at the front-line-of-care, for patient triage, or for frequent follow-ups. Very often, excluding…
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader:
Claudio Castellini
Assistive Intelligent Robotics
Project leader:
Recent legislative development, such as the European Health Data Space, expand access to anonymizied health data for various entities. While these advances offer opportunities for medical research and innovation, they also increase the risk of compromising individuals' privacy.
This project addresses the critical tension between the growing utility of health data and the need to protect individual privacy through organizational, infrastructural, and technical approaches. A key component…
Funding source: DFG / Forschungsgruppe (FOR)
Project leader: ,
Florian Knoll
Computational Imaging
Dieses Projekt ist Teil der Forschungsgruppe (FOR) "Schnelle Kartierung von quantitativen MR bio-Signaturen bei ultrahohen Magnetfeldstärken". Es konzentriert sich auf die Erweiterung, Beschleunigung und Verbesserung der Diffusions- und quantitativen Suszeptibilitäts-Magnetresonanztomographie. Das Arbeitsprogramm ist in zwei Teile gegliedert. Im ersten Teil wird ein beschleunigtes Protokoll für die klinischen Projekte der FOR vorbereitet. Im zweiten Teil sollen eine weitere Beschleunigung sowie Qu…
Funding source: Bayerische Forschungsstiftung
Project leader: ,
In Germany alone, the number of patients with chronic wound healing disorders is estimated at around 2.7 million. According to projections, the treatment of chronic wounds accounts for € 23 - 36 billion per year. Of the treatment costs for chronic wounds, 4.6 to 7.2 billion € alone are accounted for by the associated cost-intensive dressing materials. The aim of the SWODDYS project is to research the fundamentals for a new type of intelligent wound dressing for the treatment of acute and chronic wou…
Funding source: BMBF / Verbundprojekt
Funding source: Europäische Union (EU)
Project leader:
The EU project TEF-Health aims to test and validate innovative artificial intelligence (AI) and robotics solutions for the healthcare sector and accelerate their path to market. It is led by Prof. Petra Ritter, who heads the Brain Simulation Section at the Berlin Institute of Health at Charité (BIH) and at the Department of Neurology and Experimental Neurology of Charité – Universitätsmedizin Berlin. The MaD Lab of the FAU is one of the 51 participating project partners from nine European countries.
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Project leader:
The aim of this project is to develop self-supervised learning systems under biological constraints. This has the twofold advantage of providing biologically plausible computational models, as well as delivering more interpretable decision makers, capable of operating under resource-constrained conditions.
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
Project leader:
Diabetes is an overwhelming disease, directly influencing more than 422 million people worldwide who are living with this disease. Type 1 diabetes is the most severe form of the disease. The management of type 1 diabetes is especially difficult for young children and adolescents. Additionally, the most feared complication of type 1 diabetes – hypoglycemia – might occur after several hours, for example, during the night.
The DIAmond project will address the personalized and better management of t…
Funding source: Industrie
Project leader:
Biomechanical modeling and simulation are performed to analyze and understand human motion and performance. One objective is to reconstruct human motion from measurement data e.g. to assess the individual performance of athletes and customers. Another objective is to synthesize realistic human motion to study human-production interaction. The reconstruction (a) and synthesis of human motion (b) will be addressed in this research position. New algorithms using biomechanical simulation of musculoskeletal …
Funding source: Europäische Union (EU)
Project leader:
Claudio Castellini
Assistive Intelligent Robotics
Creating machines capable of directly interacting with the environment is a critical challenge in robotics. Research on robot manipulation aiming to produce machines capable of directly and autonomously interacting with the environment has increased in the last decades. Learning will be pivotal to such autonomous systems. The EU-funded IntelliMan project will focus on how a robot can efficiently learn to manipulate in a purposeful and highly performant way. The project will develop an innovative AI-Powered Manipulation System with persistent learning capabilities, able to perceive characteristics and features of the environment through a heterogeneous set of sensors, decide how to execute a task autonomously, detect failures in the task execution, and request new knowledge.
Funding source: Industrie
Project leader:
The Crossword Puzzle is a game found in many newspapers and magazines around the world. Because of this, there are many different forms (or grids) and clues formats used in them. The complexity of the clues ranges from simple questions of definitions to more complicated puns. Some clues also only require basic or historical knowledge, while others depend on an understanding of current events. In this project we aim at developing automated systems for crosswords solving, with special focus …
Funding source: Siemens AG
Project leader:
The goal of this project is to develop experimental approaches and simulation methods for biomechanical assessment of big wave surfing. This goal will be addressed in collaboration with Sebastian Steudtner and Siemens Healthineers. The methods include, but are not limited to, biomechanical movement analysis, musculoskeletal simulation, and sensor fusion.
The focus of the research activities will be centered on:
- Development of a measurement approach for biomechanical assessment of big wave surfing
- Development of efficient and accurate data processing combining inputs from several sensor systems
- Design of a biomechanical simulation model that reflects the situation during surfing
- Analysis of biomechanical measurements and simulation outcomes to provice advice for big wave surfer to improve performance.
Funding source: Siemens AG
Project leader:
The goal of this project is to develop experimental approaches and simulation methods for biomechanical assessment of big wave surfing. This goal will be addressed in collaboration with Sebastian Steudtner and Siemens Healthineers. The methods include, but are not limited to, biomechanical movement analysis, musculoskeletal simulation, and sensor fusion.
The focus of the research activities will be centered on:
- Development of a measurement approach for biomechanical…
Funding source: BMBF / Verbundprojekt
Project leader: , ,
Andreas Kist
Artificial Intelligence in Communication Disorders
Funding source: Bundesministerium des Inneren (BMI)
Funding source: Industrie
Project leader: ,
The main goal of this project is to improve the detector manufacturing for computer tomography (CT). Therefore, data is gathered during the production of a CT-detector. This data is analysed and used to develop and train a machine learning system which should find the best composition of a CT-detector. In the future, the system will be integrated into the process of CT-detector manufacturing which, in result, should further improve the image quality and the production process of CT-devices. Especially,…
Funding source: Bayerische Forschungsstiftung
Project leader:
Ziel dieses Teilprojekts ist es, datengetriebene Vorhersagen über Absatz und Haltbarkeit der Produkte unserer Industriepartner zu treffen, um Nahrungsmittelverluste zu reduzieren, während der Umsatz erhöht wird. Eine besondere Herausforderung von frischen Lebensmitteln stellt ihre kurze Haltbarkeit da, welche eine besonders präzise Absatzprognose notwendig macht. Wir adressieren dieses Problem mit einem auf Bayes‘schen maschinellen Lernen basierenden Ansatz. Bayes‘sche Methoden liefern zusätzl…
Funding source: Siemens AG
Project leader: ,
The project aims to identify areas where advanced data analysis and processing methods can be applied to aspects of computer tomography (CT) technology. Furthermore included is the implementation and validation of said methods.
In this project, we analyze machine and customer data sent by thousands of high-end medical devices every day.
Since potentially relevant Information is often presented in different modalities, the optimal application of fusion techniques is a key factor when …
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
Project leader: , ,
Project leader:
David B. Blumenthal
Biomedical Network Science
Over the last years, large amounts of molecular profiling data (also called “omics data”) have become available. This has raised hopes to identify so-called disease modules, i.e., sets of functionally related molecules constituting candidate disease mechanisms. However, omics data tend to be overdetermined and noisy; and modules identified via purely statistical means are hence often unstable and functionally uninformative. Hence, network-based disease module mining methods (DMMMs) project omi…
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
Project leader: , ,
University education in psychology, medical technology and computer science currently focuses on teaching basic methods and knowledge with little involvement of other disciplines. Increasing digitalization and the ever more rapid spread of digital technologies, such as wearable sensors, smartphone apps, and artificial intelligence, also in the health sector, offer a wide range of opportunities to address psychological issues from new and interdisciplinary perspectives. However, this requires close…
Project leader: ,
Parkinson's disease after Alzheimer's is the second most common neurodegenerative disease which mainly affects the patient's mobility and produces gait insecurity and impairment. As patients experience various, asymmetrical and heterogeneous gait characteristics, personalized medication should be at the center of attention in controlling motor complications in Parkinson's patients. potentially, inertial measurement units (IMUs) can be utilized for long-term observation of the disease progress …
Funding source: National Institutes of Health (NIH)
Project leader:
Florian Knoll
Computational Imaging
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Project leader:
The digitization of the sports sector leads to the individualization of products and services for everyday athletes. To be able to ensure this, artificial intelligence approaches are needed to know how to create personalized value for the athlete/consumer from large heterogeneous data sets.
An application example for this is the home training sector, which is gaining importance especially due to the effects of the Corona pandemic. Commercial platforms offer initial approaches to the use…
Project leader: , ,
Biopsychology is a field of psychology that analyzes how biological processes interact with behaviour, emotion, cognition, and other mental processes. Biopsychology covers, among others, the topics of sensation and perception, emotion regulation, movement (and control of such), sleep and biological rhythms, as well as acute and chronic stress.
While some software packages exist that allow for the analysis of single data modalities, such as electrophysiological data, or sleep, activity, and movement data, no packages are available for the analysis of other modalities, such as neuroendocrine and inflammatory biomarkers, and self-reports. In order to fill this gap, and, simultaneously, combine all required tools for analyzing biopsychological data from beginning to end into one single Python package, we developed BioPsyKit.
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Project leader: ,
The aim of the project is to develop a rapid automated diagnosis (using artificial intelligence) of a fusion disorder in cases of e.g. concussion by measuring the oculomotor function (eye-tracking) for three-dimensional stimuli.
Funding source: Industrie
Project leader:
Musculoskeletal (MSK) models represent the dynamics of the human body and can output many different variables i.e. joint angles, joint moments and muscle force. Personalised movement predictions provide accurate outcome variables than a generic prediction. Therefore, we would like to develop a digital twin of the MSK system, which can then be used for personalised movement predictions. Image-based personalisation is the state-of-the-art. Anthropometric variables, such as bone geometry and muscle…
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
Project leader: ,
The CRC 1483 “Empatho-Kinaesthetic Sensor Technology” (EmpkinS) investigates novel radar, wireless, depth camera, and photonics-based sensor technologies as well as body function models and algorithms. The primary objective of EmpkinS is to capture human motion parameters remotely with wave-based sensors to enable the identification and analysis of physiological and behavioural states and body functions. To this end, EmpkinS aims to develop sensor technologies and facilitate the collection of motion data for the human body. Based on this data of hitherto unknown quantity and quality, EmpkinS will lead to unprecedented new insights regarding biomechanical, medical, and psychophysiological body function models and mechanisms of action as well as their interdependencies.
Funding source: DFG / Sonderforschungsbereich (SFB)
Project leader: ,
The aim of the D02 project is the establishment of empathokinesthetic sensor technology and methods of machine learning as a means for the automatic detection and modification of depression-associated facial expressions, posture, and movement. The aim is to clarify to what extent, with the help of kinesthetic-related modifications influence depressogenic information processing and/or depressive symptoms. First, we will record facial expressions, body posture, and movement relevant to depression…
Funding source: DFG / Sonderforschungsbereich (SFB)
Project leader: , , ,
A novel postural control model of walking is explored to characterise the components of dynamic balance control. For this purpose, clinically annotated gait movements are used as input data and muscle actuated multi-body models are extended by a sensorimotor level. Neuromotor and control model parameters of (patho-)physiological movement are identified with the help of machine learning methods. Technical and clinical validation of the models will be performed. New EmpkinS measurement techniques…
Funding source: Industrie, andere Förderorganisation
Project leader: ,
The main goal of this project is to improve customer service processes and customer satisfaction.
In this project, we analzye event data from various sources like customer service activities and IoT data from high-end medical devices. The data is used in combination to implement solutions for real-time diagnosis of machine failures and to predict outcomes of customer service processes.
Methods designed and used include:
- Deep Learning
- Process Mining
- Predictive Business Process Monitoring
- Data Fusion
Funding source: DFG / Sonderforschungsbereich / Integriertes Graduiertenkolleg (SFB / GRK)
Project leader: ,
The integrated Research Training Group (iRTG) offers all young researchers a structured framework programme and supports them in their scientific profile and competence development. The diverse measures provided enable the young researchers to work on their respective academic qualifications in a structured and targeted manner. Particular attention is paid to their networking and their ability to communicate intensively and to take responsibility for their own scientific work. The doctoral…
Funding source: National Institutes of Health (NIH)
Project leader:
Florian Knoll
Computational Imaging
We propose a novel way of reconstructing medical images rooted in deep learning and computer vision that models the process how human radiologists are using years of experience from reading thousands of cases to recognize anatomical structures, pathologies and image artifacts. Our approach is based on the novel idea of a variational network, which embeds a generalized compressed sensing concept within a deep learning framework. We propose to learn a complete reconstruction procedure, including…
Funding source: DFG / Sonderforschungsbereich (SFB)
Project leader:
The extent to which a neural network can be used to effectively personalise gait simulations using motion data is explored. We first investigate the influence of body parameters on gait simulation. An initial version of the personalisation is trained with simulated motion data, since ground truth data is known for this purpose. We then explore gradient-free methods to fit the network for experimental motion data. The resulting network is validated with magnetic resonance imaging, electromyography…
Project leader: , ,
Repeated exposure to acute psychosocial stress and the associated stimulation of biological stress pathways over a period of time can promote the transition from acute to chronic stress. Unfortunately, established laboratory stress protocols are limited for repeated use due to high personnel and resource demand, creating the need for novel approaches that can be conducted at a larger scale, and, possibly, remotely.
Therefore, this project aims to develop and test novel methods for inducing acute stress without requiring extensive personnel and resource demands. The project will explore the use of digital technologies, such as virtual reality and mobile apps, to create stress-inducing scenarios that can be experienced (remotely) by study participants. The project will also investigate the use of physiological and behavioral measures to validate the effectiveness of the stress induction methods.
Funding source: Fraunhofer-Gesellschaft
Project leader:
Human movement is a complex process that depends on many factors such as body constitution, health condition, but also external factors. Joint angles, joint moments and muscle forces are variables quantifying the movement to give valuable insights about these factors. Simulation of musculoskeletal models can be used to perform detailed movement analysis to obtain these variables. The application of simulation is two-fold: Reconstruction of measured motion and prediction of new motion. Motion reconstruction…
Funding source: Industrie
Project leader:
There is a wide range of medications for RA patients, Clinical trials and real-time experience demonstrate that sometimes these treatments have adverse effects, for better benefits and later minimizing the damage, we should predict the response for each person.
This project aims to collect medical data on rheumatology arthritis, select the best factors and identify important clinical features associated with remission and then create a model to predict remissions in patients and prediction…
Funding source: DFG / Sonderforschungsbereich (SFB)
Project leader: ,
In D04, innovative, non-contact EmpkinS sensor technology using machine learning algorithms and multimodal reference diagnostics is evaluated using the example of Parkinson’s-associated sleep disorder patterns. For this purpose, body function parameters of sleep are technically validated with wearable sensor technology and non-contact EmpkinS sensor technology in comparison to classical poly-somnography and correlated to clinical scales. In an algorithmic approach, multiparametric sleep parameters a…
Funding source: National Institutes of Health (NIH)
Project leader:
Florian Knoll
Computational Imaging
The broad mission of our Center for Advanced Imaging Innovation and Research (CAI2R) is to bring together collaborative translational research teams for the development of high-impact biomedical imaging technologies, with the ultimate goal of changing day-to-day clinical practice. Technology Research and Development (TR&D) Project 1 aims to replace traditional complex and inefficient imaging protocols with simple, comprehensive acquisitions that also yield quantitative parameters sensitive to…
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Project leader:
Onlinebetrug verursachte 2019 weltweit ca. 56 Mrd. $ Schaden. Die Methoden dafür werden stetig komplexer und damit schwerer aufzudecken. Im Vorhaben sollen digitale Betrugsidentitäten und Betrugsformen durch KI in Echtzeit erkannt werden.
Funding source: Industrie
Project leader:
Project leader:
Digitization is advancing at a high pace in all areas of our lives. The development of algorithms based on artificial intelligence and more extensive and better data sets are creating new opportunities in many areas of science.
Nevertheless, it is common for many biologists, veterinarians, and animal caretakers to observe the animals manually, which is very time- and labor-expensive and comes with severe limitations. The development of an automated camera-based system seems to be self-evident.
Funding source: Industrie
Project leader:
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
Project leader:
Funding source: Industrie
Project leader:
The hearing aid of the future will be more than just an amplifying device. It may be used as fitness tracker to capture the user’s movements and activity level. Furthermore, it may be used as home monitoring device assessing the user’s vital parameters, tracking the user’s activity status or detecting falls. Hearing aids are becoming more complex and most modern hearing aids are already equipped with additional sensors such as inertial sensors. Acceleration signals are analyzed with signal proce…
Funding source: Bayerische Forschungsallianz (BayFOR)
Project leader:
Funding source: Bundesministerium des Inneren (BMI)
Project leader: ,
The complex biathlon performance consists of the components running speed, shooting stand stay (shooting stand and shooting time), and the shooting result together. Competition analyses of the last years show that the hit rate continuously increased. At the 2014 and 2018 Winter Olympics, all Placed on the podium with hit rates of over 95%. This trend continues unabated. The shooting performance of German athletes in high performance and follow-up training shows in this comparison reserves, especially…
Funding source: DFG-Einzelförderung / Heisenberg-Programm (EIN-HEI)
Project leader:
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Project leader: , , , , , ,
Andreas Maier
Department of Computer Science
Breast cancer is one of the leading causes of death in the field of oncology in Germany. For the successful care and treatment of patients with breast cancer, a high level of information for those affected is essential in order to achieve a high level of compliance with the established structures and therapies. On the one hand, the digitalisation of medicine offers the opportunity to develop new technologies that increase the efficiency of medical care. On the other hand, it can also strengthen…
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Project leader: , , , , ,
Andreas Maier
Department of Computer Science
Funding source: Virtuelle Hochschule Bayern
Project leader:
This new course is designed to familiarize engineering students with the basic procedures and tools of deep learning. A high level of application relevance is ensured by concrete examples from the respective engineering disciplines, such as mechanical engineering, materials science, production engineering, electrical engineering, process engineering or medical technology. The new course offering is designed to enable students to efficiently apply more complex methods of machine learning as a tool…
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Project leader: , ,
Mobility in atypical parkinsonism: effects of physiotherapyWider research context/theoretical frameworkParkinsonian gait disorders and reduced mobility are pivotal symptoms of Parkinson´s disease (PD) and of atypical parkinsonian disorders (APD), including Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP). Their onset signs the transition towards disability and increased mortality. While several randomized controlled trials investigated efficacy of exercise-based interventions …
Funding source: Bundesministerium für Gesundheit (BMG)
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Sensorische Anwendungen finden heutzutage durch moderne Technologien (v.a. Smartphone/Smart-Watch vermittelt) vielfach Einzug in den Alltag. In diesem Zuge stellt sich die Frage, inwieweit auch sensorische Messungen der regulären Schwangeren-Vorsorge (Herzfrequenz, Blutdruck, Sonografie und Kardiotokografie), die dem Standard nach in der Hand des Arztes oder der Ärztin liegen, in den Smart-Home Bereich transferiert werden und valide Ergebnisse liefern, sowie zukünftig die Klinik-besuche sc…
Projects of our professors before the department was established:
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013), Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
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The aim of this project is the development of a toolkit that is able to identify handwriting in real time. Using Stabilo Digipen with internal sensors that provides pen motion data in real time, machine learning algorithms are applied to track the pen movement on regular paper and digitize written sentences in real time.
Funding source: andere Förderorganisation
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Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) that is most common in young adult. The disease is due to an autoimmune reaction in which immune cells, which attack foreign pathogens, damage the body's own tissue. This project aims at identifying biomarkers in the form of gait characteristics in patients with multiple sclerosis which can be used both for the assessment of the current state of the disease and for the prediction of the further course of…
Funding source: Industrie
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The goal of this project is to develop data-based and knowledge-based methods for accurate analysis and simulation of human motion, focused on gait. Movement simulations are created by solving trajectory optimization problems, using an objective related to energy, a musculoskeletal model to model the body and muscle dynamics, and constraints to define the movement task. We use data-based approaches to improve musculoskeletal models and simulation accuracy. With our research, we aim to better understand…
Project leader: , ,
Many studies investigating the cortisol awakening response (CAR) suffer from a lack of precise and objective methods for assessing the awakening and saliva sampling times. Failure to correctly report times or to adhere to the study protocol, which is common in unsupervised real-world studies, can lead to a measurement bias on CAR quantification and can even contribute to erroneous findings in psychoneuroendocrinological (PNE) research.
To address this gap in available methodology, we developed CARWatch. CARWatch is a framework to support objective and low-cost assessment of cortisol samples in unsupervised, real-world environments. It consists of an Android application that schedules sampling times and tracks them by scanning a barcode on the respective sampling tube as well as a Python package that provides tools to configure studies, prepare the study materials, and process the log data recorded by the app.
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
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Im Projekt soll ein elektronisches Mikrosystem entwickelt und erforscht werden, dessen
Energieversorgung und Sensorik auf piezoelektrischem PVDF-Material basiert und das drahtlos mit der
Außenwelt kommuniziert. Wissenschaftliche Veröffentlichungen zeigen die potentielle Energieausbeute, die
für den energieautarken Betrieb eines integrierten, drahtlosen Mikrosystems benötigt wird. Neben dieser
Energy Harvesting Funktion kann die PVDF-Technologie auch als Drucksensor verwendet wer…
Funding source: Europäische Union (EU)
Project leader: , , ,
Optimal treatment of the impaired mobility resulting from ageing and chronic disease is one of the 21st century's greatest challenges facing patients, society, governments, healthcare services, and science. New interventions are a key focus. However, to accelerate their development, we need better ways to detect and measure mobility loss. Digital technology, including body worn sensors, has the potential to revolutionise mobility assessment. The overarching objectives of MOBILISE-D are threefold:…
Funding source: Bundesministerium für Wirtschaft und Energie (BMWE)
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Der Digital Sports Hub ist eine vom Bundesministerium für Wirtschaft und Energie (BMWi) geförderte Projektinitiative. Dahinter steckt ein ganzes Team aus dem Institut für experimentelle Psychophysiologie GmbH, der Friedrich-Alexander-Universität Erlangen-Nürnberg mit dem Lehrstuhl Maschinelles Lernen und Datenanalytik, dem Bundesinstitut für Sportwissenschaft, Wearables Technology, SAP, der Innovationsmanufaktur GmbH und Medical Valley. Das Ziel des Digital Sports Hub ist der Aufbau eines deuts…
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
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Dysregulations of the biological stress systems can lead to various negative psychological and physiological health outcomes, such as cardiovascular diseases, inflammation, depression, and anxiety. Considering the severity of these consequences and the overall number of stress-related illnesses, there is a dire need for developing methods to manage this threat to our health, for instance, by developing interventions that prevent our stress systems from deregulating.
Thus, this project aims to investigate various interventions that can be used to manage acute psychosocial stress responses and explore the effectiveness of these interventions in different populations. The ultimate goal of the project is to develop evidence-based interventions that can be used to manage acute psychosocial stress responses in various populations, with the aim of improving mental health and well-being. A special focus is on interventions that can easily be integrated into daily-life tasks.
The project's findings could have significant implications for the development of personalized stress-management interventions that can be tailored to individual needs and preferences.
Funding source: Industrie
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Bernhard Kainz
Image Data Exploration and Analysis
Funding source: Virtuelle Hochschule Bayern
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This course offers an overviewof some of the most widely used machine learning methods that are necessary toknow in order to be able to work on data science applications. We present thenecessary fundamental for each topic and provide coding exercises in order topractice the models.
The course includes:
1) The common practicesfor data collection, anomaly detection and signal fusion.
2) Teaching differenttasks regarding regression, classification, and dimensionality reduction usingmethods…
Funding source: EU - 8. Rahmenprogramm - Horizon 2020
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HOOP is an mHealth platorm for Parkinson's disease patients' training and rehabilitation, based on music and haptic stimulation, to be used in the patient’s home employing a set of sensors and exercises to evaluate the performance. HOOP will become a commercial product which will enable Parkinson's patients' rehabilitation at home in a cost-effective basis. It is based on the use of acoustic and haptic stimulation techniques during the performance of motor (upper and lower limbs) and nonmotor exercises (cognitive tests). According to the high versatility of the product, HOOP will create a sustainable business plan which will introduce into several target markets, including but not limited to Parkinson’s patients.
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
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The patient-centred healthcare ecosystem (PHE) is a future digital healthcare model where all healthcare stakeholders will eventually transition from an isolated approach to a collaborative approach around the patient. Among other benefits, the PHE will enable individuals to take control of their health information in a confidential and secure environment. Currently evolving PHEs either use a centralized database or blockchain technology for storing medical records.
On the one hand, healthcare…
Funding source: Europäische Union (EU)
Project leader: ,
Parkinson’s disease (PD) is the most common neurodegenerative movement disorder (10 mio. patients worldwide) with increasing prevalence in western societies. Gait impairment and falls strongly reduce quality of life for PD patients, lead to severe comorbidities, and cause substantial healthcare costs. Scientific evidence shows that adequate treatment may increase mobility, reduce risk-of-falling, prevent falls, and reduce cost. However, technologies to comprehensively monitor gait&falls are not available or fail to qualify as a medical service or product, and can therefore not be applied clinically in multidisciplinary healthcare concepts to improve PD patient mobility.
This project will generate the first comprehensive gait&fall assessment battery integrated into a multidisciplinary healthcare concept by establishing an innovative digital health pathway for gait&falls in PD. Existing well-characterised patient cohorts and state-of-the-art healthcare concepts will be used in a Living Lab to develop, test, and validate sensor-based movement assessments for application in clinical studies and patient care, ultimately preventing falls by moving PD healthcare into digital health.
Funding source: Industrie
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Drivetrain technology companies are presented to the emerging challenge to meet the comprehensive demands of modern systems by consolidating the competencies of the latest developments in the field of machine learning. The efforts for Schaeffler to establish those competencies are easier to accomplish by including a strong research partner. This partner for the given project will be the Machine Learning and Data Analytics Lab of the FAU. As a result of this project Schaeffler and the business unit obtain methodological competences in general as well as new approaches of deep learning algorithms for signal analysis in particular.
Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Project leader: ,
Andreas Maier
Department of Computer Science
The goal of this project is the investigation of multimodal methods for the evaluation of interventional workflows in the operation room. This topic will be researched in an international project context with partners in Germany and in Brazil (UNISINOS in Porto Alegre). Methods will be developed to analyze the processes in an OR based on signals from body-worn sensors, cameras and other modalities like X-ray images recorded during the surgeries. For data analysis, techniques from the field of…
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
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The aim of the project is to design and implement a system that is able to holistically assess athletes. Therefore, novel individualised digital services, products and tools to enable a holistic mental and physiological performance optimisation will be developed. This is an improvement compared to current state of the art methods, since these approaches only consider physiological data for optimising performance.
By applying modern computation methods (e.g. deep learning using Convolutional Neural Networks) common physiological- will be fused with cognitive-performance parameters (e.g. executive functions or perceptual-cognitive skills). As a result, a holistic digital representation will be derived. Additionally, behavioural data from alternative data sources like user patterns from social media will be used to facilitate a prediction of different performance aspects (e.g. motivation or development). In a final step an algorithm will be implemented, that assures autonomous self-improvement of the system beyond the duration of the project.
For determining relevant cognitive indicator, different sensor signals will be analysed (e.g. ECG, EEG, Eye-Tracking) and related to the physiological data. To do so standardised assessment environments will be created by developing innovative interaction concepts. Different approaches along the Reality-Virtuality spectrum will be explored to facilitate user friendly products and services.
Funding source: Fraunhofer-Gesellschaft
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This project investigated methods and techniques of machine learning in the field of localization. Deep neural networks have been used to model nonlinear signal propagation in radio-based localization to enable position determination even in difficult metallic environments. Furthermore, it was investigated to what extent the temporal consideration of radio signals by means of recurrent neural networks can provide an added value directly in the localization algorithm and to what extent these can…
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Funding source: Industrie
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The main goal of this project is to improve the overall system quality and customer satisfaction.
In this project, we analyze IoT data (machine logs and sensory data) sent by thousands of high-end medical devices every day. The extracted information can include physical parameters and additional extracted event patterns. This data can be used to predict the failure of specific components and correlate malfunction to machine usage. As a consequence, system stability…
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Teamworkand communication between group members get increasingly important in the taskof solving complex problems [1]. To measure the social interactionand the dynamic of groups, members of the MIT Media Lab have developed an opensource sensor platform called “Open Badges”. The badges are usedto determine several parameters like vocal activity, proximity to …
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
Project leader: , ,
The ability to walk defines human nature and limits the mobility, independence, and quality of life. Falls are the leading cause of both fatal and nonfatal injuries among older adults, causing severe injuries such as hip fractures, head trauma, and death. Increased fall risk is a key symptom in Parkinson’s disease (PD), limiting the independency and mobility of patients.
So far, no validated technical solutions exist to identify the individual’s rising fall risk before the first fall occ…
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
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Vision Impairmentslike amblyopia can be treated by digital therapies using virtual reality systems.These therapies help to avoid or at least reduce the lifelong consequences dueto developmental disorders. Amblyopia is a functional vision impairment of asingle eye based on insufficient development of the visual system in earlychildhood. In Central Europe the prevalence of amblyopia is about 5 – 6%.Amblyopia is the main reason for vision impairments during the first 45 yearsof life. Occlusion t…
Funding source: Siemens AG
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Autonomous applied logistics, especially in context of Industry 4.0, is an important factor for fully automated systems. These systems involve autonomous operation of loading and unloading processes, safety measures by detecting persons in danger zones and the general optimization of the logistical processes.
Especially, the application of these systems in a harbor environment, where different systems from all over the world interact, increases the complexity of the loading and unloading processes. The aim of this research project is to determine the feasibility of automating the unloading process by segmenting, classifying and fitting laser range data by using Machine Learning techniques.
Funding source: Industrie
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In this project, we investigate musculoskeletal modeling and simulation to analyze and understand human movement and performance. Our objective is to reconstruct human motion from measurement data for example for medical assessments or to predict human responses for virtual product development.
Reconstruction of Human Motion: Biomechanical analysis using wearable systems
Inertial sensor systems provide the possibility of cheap gait analysis in everyday life. …
Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Project leader: ,
Die Digitalisierung birgt große Potenziale zur Steigerung der Ressourceneffizienz industrieller Produktionsprozesse. Durch Technologien im Kontext von Industrie 4.0 können produktionsnahe Daten kurzzyklisch erfasst und aggregiert werden. In Anbetracht der dadurch zunehmenden Datenkomplexität und des Datenvolumens stehen Mitarbeiter jedoch vor der Herausforderung, diese Daten zu analysieren und zu interpretieren sowie die Nachhaltigkeit der eingeleiteten Maßnahmen zu bewerten, wobei die kognitiven Fäh…
Funding source: Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst (ab 10/2013)
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Immersion in a digital world, controlling technology in your home from the other side of the world, using tiny sensors knitted into shirts to track vital parameters all day long – these all sound rather futuristic, however are increasingly becoming a reality in our everyday lives. Driven by the miniaturization of electrical devices, our clothing and, in fact, our environment is becoming smarter, enabling a fantastic platform for innovation. Students of the Friedrich-Alexander-University Erlangen-Nuremberg (FAU) are able to use this platform by visiting the Innovation Lab for Wearable and Ubiquitous Computing, funded by the Center for Digitalization Bavaria. (ZD.B - https://zentrum-digitalisierung.bayern/).
The Innovation Lab is located at the Technical Faculty of the FAU in Prof. Bjoern Eskofier’s Machine Learning and Data Analytics Lab, providing the students with the infrastructure needed to develop innovative prototypes. The ideas for these prototypes will originate from the students, researchers in related fields and industry partners.
Funding source: Industrie
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Performance Analysis in team sports is an emerging field in computer science. In Europe's leagues, a large amount of data is recorded during the season. Based on methods of machine learning and signal processing an automated, fast and accurate analysis of soccer is possible.
In this project, the performance of a single player and the behavior of the whole team (e.g. tactics) is calculated based on position and inertial sensor data.
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
Project leader: ,
Gangstörungen und Mobilitätseinschränkungen treten bei einer Vielzahl chronischer Erkrankungen auf und verursachen in Deutschland ca. 20% der Kosten im Gesundheitswesen. Die Veränderung des Gangbildes ist charakteristisch für den Fortschritt dieser Erkrankungen. Sie wird durch die behandelnden Ärzte zur diagnostischen Bewertung der Erkrankung und zur Unterstützung der therapeutischen Entscheidungen auch heute schon visuell befundet.
Das Parkinson Syndrom (PS) zählt zu den klassischen chronischen …
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
Project leader: ,
The project ‚Schlafkur mit Begleitanalyse‘ aims to close a major gap in the market and offer citizens with sleep difficulties an accessible, effective and digitally supported solution. According to the Health Report 2017 of the DAK Gesundheit, 80% of the working population in Germany sleep poorly. One in ten workers suffer from sleep disorders, thus affecting over 1.5 million people in Bavaria.
We scientifically evaluate the combination of intelligent sleep intervention technology for sleep support, an intelligent sensor for sleep diagnostics and an individualized, IT-based intervention program for each participant with sleep problems. We plan to offer an efficient, low cost service to improve sleep as part of the digital health care industry in Bavaria.
The target group is mainly those in the 30-65 year-old age range of working persons suffering from non-organic sleep disorders. Because there are many types of non-organic sleep disorders: from severe, long-term insomnia to sleep disorders during menopause to mild, recurring problems with falling asleep or remaining so. Usually, these symptoms are independent of a true sleep disorder. Despite a considerable number of affected people, there is a lack of appropriate (self-) analysis and insight, due, in part, to poor social recognition of sleep disorders as well as a lack of diagnosis of the widespread disease "insomnia". This means that the mild to moderate limitations of the affected people do not have access to the appropriate medical or psychological treatment. This project now addresses this population aiming to improve the quality of life and to prevent chronic sleep disorders in the medium term; we provide the patient with individual and professional coaching to recover a restful and peaceful sleep.
Project leader: , ,
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
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Funding source: Bundesministerium für Forschung, Technologie und Raumfahrt (BMFTR)
Project leader: ,
Dieses Vorhaben zielt darauf ab, neben neuen universitären Lehrveranstaltungen fürMasterstudierende, klein- und mittelständische Unternehmen (KMU) dabei zuunterstützen, die im Rahmen der Digitalisierung verfügbaren Daten mittelsmaschinellem Lernen (ML) sinnvoll zu nutzen und damit Entscheidungsprozesse zuunterstützen. Durch die thematische Aufstellung der Projektpartner an der FAUstammen Seminare, Praktika und Vorlesungen aus unterschiedlichen Säulen derInformatik und bieten somit vielfältige Vertief…
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In this project wearables for sports or rehabilitation are developed. They can be used for training control or analysis.
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
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The aim of this project is the development of a digital, visual perceptual learning system (D-VPL) with gesture recognition and telemedical link to ophthalmologists for visually impaired elderly people, dementia prevention, and patients with traumatic brain injury. The users will react by using gestures to moving objects that will be presented in virtual reality or on a 3-D display. The combination of D-VPL and gesture control leads to a dual task training, and the telemedical link enables applications…
Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
Project leader:
The increasing complexity of radiotherapy poses a serious challenge toquality management processes. Proper functioning of devices, precisecontrol of process variables and thus ultimately the safety of thepatient regarding exposure to radiation must be ensured. However, Thecomplexity of the radiotherapy workflow lead to seriouos accidents inthe past. Up to date, the quality of only partial radiotherapy workflowsteps is properly assessed and the workflow as a whole is not assessed.The goal of this…
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The ability to relax is sometimes challenging to achieve, nevertheless it is extremely important for mental and physical health, particularly to effectively manage stress and anxiety. For this reason, we propose a virtual reality experience that integrates a wearable, low-cost EEG headband and an olfactory necklace that passively promotes relaxation.
Due to the increasing quality and availability of low-cost EEG systems they can be applied in such a scenario to measure the current level of relaxation. Therefore, we introduce new algorithms for real-time mental state recognition based on an entropy-based signal processing approach.
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Objective health data about subjects outside of the laboratory is important in order to analyse symptoms that cannot be reproduced in the laboratory. A simple daily life example would be how stride length changes with tiredness or stress. In order to investigate this we must be able to accurately segment a stride from daily living data in order to have an accurate measure of duration and distance. State-of-the-art methods use separate segmentation and classification approaches. This is inaccurate for segmentation of an isolated activity, especially one that is not repeated. This could be solved using a model that is based on the sequence of phases within activities. Such a model is a graphical model. Currently we are working with Conditional Random Fields and Hierarchical Hidden Markov Models on daily living data. The applications will include sports as well as daily living activities.
Project leader: ,
Digitalization of sports is also taking place in table tennis. This is caused by body-worn Wearables. This project captures, analyzes and processes the motion and movement of players, ball characteristics and other interesting parameters during table tennis games and exercises. In contrast to common standard camera-based analysis within predefined laboratory environments used for most ball sports, only sensors mounted on the racket are included. All necessary electronics should ideally be hidden inside the racket, that the user does not feel affected. For motion analysis, mainly inertial sensors (accelerometers and gyroscopes) are used, as well as magnetometers for the absolute alignment in space and specific piezoelectric sensors for vibration detection. First, the acceleration, the angular velocity and absolute orientation of the racket are measured to classify the stroke type using pattern recognition algorithms. It is possible to differentiate between forehand and backhand stroke types and various spin types. In addition, the ball impact event is verified by the vibration sensors. Afterwards, the resulting ball speed and spin are estimated shortly after this impact. Finally, the point of impact on the racket is localized by triangulation methods, similar to epicenter localization during earthquakes. All data is calculated on the embedded microcontroller and transferred to a mobile device, such as an Android smartphone via Bluetooth. There, the data is provided to the player as feedback for training support or statistics.
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Funding source: Bayerische Staatsministerien
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Die Europäische Metropolregion Nürnberg (EMN) bietet eine weltweit einzigartige Konstellation für die industrienahe Forschung im Bereich eingebetteter Systeme für Sport- und Fitnessanwendungen. Das Lab Fitness@ESI wird breit aufgestellt. Es gilt sowohl Themen des Spitzen- und Breitensports zu bedienen, als auch Wege aufzuzeigen, zentrale Themen des stetig an Bedeutung gewinnenden Marktes für Sportmöglichkeiten behinderter Menschen zu erleichtern. Weiterhin werden Bereiche von der persö…
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DailyHeart is a system for vertigo assessment during daily-life activities. It addresses the whole pipeline from data acquisition (via unobtrusive body-worn sensors) over data processing to data visualization and potential feedback (via Android application).
The main idea is to assess the autonomous nervous system (ANS) during the orthostatic reaction, characterized by an increase in heart rate after posture transitions, e.g. from lying or sitting to standing. Whereas the ANS of healthy subjects can easily adapt to these posture changes, people suffering from ANS disorders (e.g. Parkinson’s Disease) experience a considerably heart rate response and a decrease in blood pressure, potentially leading to vertigo and syncope.
Clinical results have shown that multiple measurements throughout the day can already indicate a possible disorder of the ANS. For that reason, DailyHeart aims to transfer the clinical assessment into the home environment by detecting posture changes during daily-life activities and thus trigger an HRV and BP analysis for effectively assessing vertigo and thus provide a recommendation of consulting a medical expert.
Funding source: Stiftungen
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Funding source: Bayerisches Staatsministerium für Wirtschaft, Infrastruktur, Verkehr und Technologie (StMWIVT) (bis 09/2013)
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Body Sensor Networks are getting more and more important in sports and health. Currently, various isolated applications exist that use body sensors to assist athletes and monitor elderly people. Systems like the adidas miCoach and Nike+ prove the potential of information and communication engineering technology for manufacturers of sports equipment. The perfect product for a leading position in this market would be a central, flexible and generic wearable computing platform instead of isolated…
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RoboCup is a international initiative to promote research in artificial intelligence and autonomous mobile robots. Each year the RoboCup Foundation hosts international tournaments where top research groups of Universities from the whole world participate. Since 2008, the University of Erlangen-Nuremberg also has its own RoboCup team that participates in the small-size league. This league is one of the smallest and fastest RoboCup leagues. Five wheeled robots per team are playing on a field of about 6m x 4m. The maximum size for each robot is 18cm in diameter and a height of 15 cm. The robots get information about the current game situation from two cameras above the field and an external computer, which communicates with the robots via a wireless link. In Erlangen the team is organized as an interdisciplinary student project at the Technical Department. The main goals of this project are to foster creative ideas and team work among technical students from electrical engineering, mechatronics and computer science. Research topics include topics from pattern recognition, embedded systems and artificial intelligence. In the scope of this project the Pattern Recognition Lab employs statistical estimation techniques and tries to extend them towards automotive applications. To promote the project, a non-profit organisation called "Robotics Erlangen e.V." was founded in 2008. In this organization team members as well as supporters of the group are brought together. The project is funded in part by tuition fees as well as private and industry donations.