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Testing and Experimentation Facility for Health AI and Robotics
(Third Party Funds Group – Sub project)
Overall project: Testing and Experimentation Facility for Health AI and Robotics
Term: 1. January 2023 - 31. December 2027
Funding source: Europäische Union (EU)
URL: https://www.tefhealth.eu/
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.
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Erarbeitung der Studienkonzeption, Medizinisch wissenschaftlich beratende Funktion
(Third Party Funds Group – Sub project)
Overall project: Digitale Gesundheitsanwendung zur Therapie von Inkontinenzpatienten
Term: 1. January 2023 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
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.
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DIAMond - diabetes type 1 management with personalized recommendation using
data science
(Third Party Funds Single)
Term: 1. September 2022 - 29. February 2024
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
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Applied Data Science in Digital Psychology
(Third Party Funds Single)
Term: 1. September 2022 - 31. August 2026
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
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 cooperation between the disciplines of psychology and technical disciplines such as medical technology and computer science to enable the necessary knowledge transfer. Especially in these disciplines, there is a considerable need for innovative and interdisciplinary teaching concepts and research projects that teach the adequate use of digital technologies and explore the application of these technologies to relevant issues in order to enable better care in the treatment of people with mental disorders.
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Multimodal Machine Learning for Decision Support Systems
(Third Party Funds Single)
Term: since 1. June 2022
Funding source: Siemens AG
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 extracting insights.
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Maschinelles Lernen für Haltbarkeits- und Absatzprognosen und Bestimmung der Authentizität
(Third Party Funds Group – Sub project)
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 zusätzlich zu einer punktuellen Vorhersage (z. B. über den Absatz) auch ein Maß über die Unsicherheit dieser Vorhersage.
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Machine Learning for CT-Detector Production
(Third Party Funds Single)
Term: since 1. April 2022
Funding source: Industrie
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, the warehouse utilization and the first-pass-yield should be enhaced. The project is realized in cooperation with Siemens Healthineers Frochheim.
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Biologically-inspired self-supervised systems
(Own Funds)
Term: since 1. January 2022
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.
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Trusted Ecosystem of Applied Medical Data eXchange; Teilvorhaben: FAU@TEAM-X
(Third Party Funds Group – Sub project)
Overall project: Trusted Ecosystem of Applied Medical Data eXchange (TEAM-X)
Term: 1. January 2022 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
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Adaptive AI Systems in Sport
(Third Party Funds Single)
Term: 1. December 2021 - 31. May 2024
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
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 of immersive media but fail to generate individualization of content by analyzing heterogeneous data sources for the user.
The project will therefore investigate mechanisms for user engagement and motivation. Based on this, a comprehensive adaptive AI system for predicting individual goal achievement will be developed and fused with additional data sources. Based on the predictions, a framework for designing stimulus-driven real-time systems for individualizing immersive user interfaces will be defined. The integration of the resulting subsystems into a high-fidelity prototype enables the transfer to further application domains.
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dhip campus-bavarian aim
(Third Party Funds Group – Overall project)
Term: 1. October 2021 - 30. September 2027
Funding source: Industrie
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Novel Methods for Remote Acute Stress Induction
(Own Funds)
Term: since 1. September 2021
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.
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Personalized prediction of medications responses in patients with rheumatoid arthritis using Machine Learning algorithms
(Third Party Funds Group – Sub project)
Overall project: dhip campus-bavarian aim
Term: 1. September 2021 - 30. August 2024
Funding source: Industrie
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 of treatment response and course of activity for each patient using machine learning methods. This project could help in preventing wrong prescriptions and time-wasting before disease progression.
We want to reach the aim by using medical data collected and recorded by rheumatologists from patient characteristics, disease courses, laboratory data, and medication data. Our partners from the medicine side are helping to collect and access existing data. The partners in MaD-Lab carry out Machine learning and data analytics approaches on them to find a remission or development of the prognostic model.
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BioPsyKit – An Open-Source Python Package for the Analysis of Biopsychological Data
(Own Funds)
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.
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Empatho-Kinaesthetic Sensor Technology
(Third Party Funds Group – Overall project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://empkins.de/
The proposed CRC “Empathokinaesthetic Sensor Technology” (EmpkinS) will investigate 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.The main focus of EmpkinS is on capturing human motion parameters at the macroscopic level (the human body or segments thereof and the cardiopulmonary function) and at the microscopic level (facial expressions and fasciculations). The acquired data are captured remotely in a minimally disturbing and non-invasive manner and with very high resolution. The physiological and behavioural states underlying the motion pattern are then reconstructed algorithmically from this data, using biomechanical, neuromotor, and psychomotor body function models. The sensors, body function models, and the inversion of mechanisms of action establish a link between the internal biomedical body layers and the outer biomedical technology layers. Research into this link is highly innovative, extraordinarily complex, and many of its facets have not been investigated so far.To address the numerous and multifaceted research challenges, the EmpkinS CRC is designed as an interdisciplinary research programme. The research programme is coherently aligned along the sensor chain from the primary sensor technology (Research Area A) over signal and data processing (Research Areas B and C) and the associated modelling of the internal body functions and processes (Research Areas C and D) to the psychological and medical interpretation of the sensor data (Research Area D). Ethics research (Research Area E) is an integral part of the research programme to ensure responsible research and ethical use of EmpkinS technology.The proposed twelve-year EmpkinS research programme will develop novel methodologies and technologies that will generate cutting-edge knowledge to link biomedical processes inside the human body with the information captured outside the body by wireless and microwave sensor technology. With this quantum leap in medical technology, EmpkinS will pave the way for completely new "digital", patient-centred diagnosis and therapeutic options in medicine and psychology.Medical technology is a research focus with flagship character in the greater Erlangen-Nürnberg area. This outstanding background along with the extensive preparatory work of the involved researchers form the basis and backbone of EmpkinS.
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EmpkinS iRTG - EmpkinS integrated Research Training Group
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich / Integriertes Graduiertenkolleg (SFB / GRK)
URL: https://www.empkins.de/
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 researchers are supervised by two project leaders.
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Sensorbasierte Bewegungs- und Schlafanalyse beim Parkinson-Syndrom
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
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 and sleep patterns are then evalulated in correlation to movement, cardiovascular and sleep phase regulation disorders.
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Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
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 with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
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Erforschung der posturalen Kontrolle basierend auf sensomotorisch erweiterten muskuloskelettalen Menschmodellen
(Third Party Funds Group – Sub project)
Overall project: Empathokinästhetische Sensorik
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
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 are to be transferred to the developed models as soon as possible.
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Holistic customer-oriented service optimization for fleet availability
(Third Party Funds Single)
Term: 1. June 2021 - 31. May 2024
Funding source: Industrie, andere Förderorganisation
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Symptom detection and prediction using inertial sensor-based gait analysis
(Own Funds)
Term: since 1. May 2021
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 and estimating gait parameters. This project is dedicated to detecting and possibly predicting the motor symptoms of Parkinson's disease such as Bradykinesia, Dyskinesia, and the freeze of gait. This also includes the improvement of the existing gait analysis algorithms to fit the parkinsonian gait more accurately, which is the basis of symptom detection.
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Das Okulomotor Test System in der Virtuellen Realität -- Telemedizinische Detektion von Gehirnerschütterungen in VR
(Third Party Funds Single)
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.
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Tracking-based museum visitor research
(Third Party Funds Single)
Term: 15. February 2021 - 31. December 2023
Funding source: Industrie
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Video-based Re-Identification for Animals
(Own Funds)
Term: since 1. January 2021
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.
The automated analysis of video footage from surveillance cameras is a possibility to evaluate activity/inactivity and stereotypy of the animals as well as their enclosure use over long periods of time and thus allows an objective assessment. In the project's next phase, further behavior analysis will be added (for example, secondary behavior such as feeding, playful behavior, or interaction). The most challenging step for such a system is the re-identification (reID) of individual animals in every camera perspective
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Individuelle Sportschuhempfehlung durch maschinelles Lernen
(Non-FAU Project)
Term: since 1. January 2021
Even though sport shoe models can differ in properties (fit, cushioning, bending stiffness etc.) the products come out of mass production and are made for generic groups of athletes. Humans are highly individual and research has shown that athletes are responding individually to certain shoe characteristics. Goal of this research is to create predictions for individual athletes to benefit from the right choice of shoes - be it through increased comfort, better performance or injury prevention.
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Mobility in atypical parkinsonism: a randomized trial of physiotherapy
(Third Party Funds Single)
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 for PD, for APD this area remains thus far widely unexplored. Results of our pilot study investigating efficacy of a physiotherapy program in APD showed improvements of gait parameters as reflected by instrumented gait analysis in lab. Current advances in development of wearable sensor-based technologies now reach clinical applicability also under remote unsupervised conditions (home-monitoring).Hypotheses/research questions /objectivesIn this randomized controlled trial we expect to detect greater improvement of gait performance and mobility in PD and APD patients by gait-focused versus standard physiotherapy and home-training. In particular, we aim to investigate whether:1) Gait-focused versus standard physiotherapy and home-based exercise improve lab and home-based gait parameters, physical activity and clinical rating scales in PD and APD patients.2) PD, MSA and PSP patients differ in their response to gait-focused versus standard physiotherapy and home-based exercise as detected by lab and home-based gait parameters, physical activity and clinical rating scales.Approach/methodsThe trial will be multicentric, randomized, double-blinded and controlled. The intervention to test consists of an inpatient gait-focused physiotherapy followed by an unsupervised gait-focused home-based training program. The control group receives a standard physiotherapy/home-based training, which addresses parkinsonian features without focusing on gait. Lab gait analysis is performed by shoe-insole-sensors to retrieve objective gait parameters (e.g. gait velocity, stride length etc.). Home-monitoring is performed using similar shoe-sensors and one sensor at low-back position allowing for the analysis of routine activities such as sitting, lying, standing and walking.Level of originality/innovationImproving gait disorders in PD and APD patients by gait focused PT and home-based exercise increases patients´ independence and forestalls the risk of falls representing a great achievement for patients. This study lays the foundation for the development of a telemedical approach by which patient groups can be included in clinical trials remote from expert centers.Primary researchers involvedGregor Wenning (lead-PI), Cecilia Raccagni (study coordinator), Jochen Klucken (Erlangen-PI), David Benninger (Lausanne-PI) and Bas Bloem (Nijmegen-PI).Björn Eskofier (Erlangen) and Kamiar Aminian (Lausanne) will be responsible for the technical part concerning the sensor system.
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Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie undRückfallvermeidung am Beispiel von Brustkrebs - DigiOnko
(Third Party Funds Single)
Term: 1. October 2020 - 30. September 2024
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
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Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie und Rückfallvermeidung am Beispiel von Brustkrebs
(Third Party Funds Single)
Term: 1. October 2020 - 30. September 2024
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
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 patient compliance by improving information and patient integration through electronic health applications. Thus, a reduction in mortality and an improvement in quality of life can be achieved. Within the framework of this project, digital health programmes are going to be created that support and complement health care. The project aims to provide better and faster access to new diagnostic and therapeutic procedures in mainstream oncology care, to implement eHealth models for more efficient and effective cancer care, and to improve capacity for patients in oncologcal therapy in times of crisis (such as the SARS-CoV-2 pandemic). The Chair of Health Management is conducting the health economic evaluation and analysing the extent to which digitalisation can contribute to a reduction in the costs of treatment and care as well as to an improvement in the quality of life of breast cancer patients.
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Activity Recognition using IMU Sensors integrated in Hearing Aids
(Third Party Funds Single)
Term: 1. October 2020 - 31. March 2024
Funding source: Industrie
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 processing algorithms to enhance speech intelligibility and audio quality. Furthermore, inertial sensors may be used to analyze the user’s movements and physical activity. Hearing aid amplification settings may be adapted according to the current activity. Moreover, given the user's explicit consent, activity recognition enables a long-term tracking of the user’s daily activity status.
The objective of this project is to investigate automatic activity recognition based on inertial sensor data. Therefore, data of different activities will be recorded using the IMU sensor integrated in the hearing aids. The hearing aids are provided by the cooperation partner WS Audiology. Machine learning algorithms will be developed to automatically classify different activity patterns.
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Biomarkers for immunotherapy in multiple sclerosis patients
(Third Party Funds Single)
Term: 1. October 2019 - 30. September 2023
Funding source: andere Förderorganisation
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 the disease in the form of relapses.
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Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement
(Third Party Funds Single)
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: to deliver a valid solution (consisting of sensor, algorithms, data analytics, outcomes) for real-world digital mobility assessment; to validate digital outcomes in predicting clinical outcome in chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture recovery and congestive heart failure; and, to obtain key regulatory and health stakeholder approval for digital mobility assessment. The objectives address the call directly by linking digital assessment of mobility to clinical endpoints to support regulatory acceptance and clinical practice. MOBILISE-D consists of 35 partners from 13 countries with long, successful collaboration, combining the requisite expertise to address the technical and clinical challenges. To achieve the objectives, partners will jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes can successfully predict relevant clinical outcomes and provide a better, safer and quicker way to arrive at the development of innovative medicines. MOBILISE-D's results will directly facilitate drug development, and establish the roadmap for clinical implementation of new, complementary tools to identify, stratify and monitor disability, so enabling widespread, cost-effective access to optimal clinical mobility management through personalised healthcare.
We conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.
Research projects
Current projects
Testing and Experimentation Facility for Health AI and Robotics
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2027
Funding source: Europäische Union (EU)
URL: https://www.tefhealth.eu/
Erarbeitung der Studienkonzeption, Medizinisch wissenschaftlich beratende Funktion
(Third Party Funds Group – Sub project)
Term: 1. January 2023 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)
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.
DIAMond - diabetes type 1 management with personalized recommendation using data science
(Third Party Funds Single)
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
Applied Data Science in Digital Psychology
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wissenschaft und Kunst (StMWK) (seit 2018)
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 cooperation between the disciplines of psychology and technical disciplines such as medical technology and computer science to enable the necessary knowledge transfer. Especially in these disciplines, there is a considerable need for innovative and interdisciplinary teaching concepts and research projects that teach the adequate use of digital technologies and explore the application of these technologies to relevant issues in order to enable better care in the treatment of people with mental disorders.
Multimodal Machine Learning for Decision Support Systems
(Third Party Funds Single)
Funding source: Siemens AG
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 extracting insights.
Maschinelles Lernen für Haltbarkeits- und Absatzprognosen und Bestimmung der Authentizität
(Third Party Funds Group – Sub project)
Term: 1. April 2022 - 31. December 2023
Funding source: Bayerische Forschungsstiftung
URL: https://www.bayfor.org/de/unsere-netzwerke/bayerische-forschungsverbuende/forschungsverbuende/project/shield/teilprojekt-3-masch
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 zusätzlich zu einer punktuellen Vorhersage (z. B. über den Absatz) auch ein Maß über die Unsicherheit dieser Vorhersage.
Machine Learning for CT-Detector Production
(Third Party Funds Single)
Funding source: Industrie
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, the warehouse utilization and the first-pass-yield should be enhaced. The project is realized in cooperation with Siemens Healthineers Frochheim.
Biologically-inspired self-supervised systems
(Own Funds)
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.
Trusted Ecosystem of Applied Medical Data eXchange; Teilvorhaben: FAU@TEAM-X
(Third Party Funds Group – Sub project)
Term: 1. January 2022 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
Adaptive AI Systems in Sport
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
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 of immersive media but fail to generate individualization of content by analyzing heterogeneous data sources for the user.
The project will therefore investigate mechanisms for user engagement and motivation. Based on this, a comprehensive adaptive AI system for predicting individual goal achievement will be developed and fused with additional data sources. Based on the predictions, a framework for designing stimulus-driven real-time systems for individualizing immersive user interfaces will be defined. The integration of the resulting subsystems into a high-fidelity prototype enables the transfer to further application domains.
dhip campus-bavarian aim
(Third Party Funds Group – Overall project)
Funding source: Industrie
Novel Methods for Remote Acute Stress Induction
(Own Funds)
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.
Personalized prediction of medications responses in patients with rheumatoid arthritis using Machine Learning algorithms
(Third Party Funds Group – Sub project)
Term: 1. September 2021 - 30. August 2024
Funding source: Industrie
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 of treatment response and course of activity for each patient using machine learning methods. This project could help in preventing wrong prescriptions and time-wasting before disease progression.
We want to reach the aim by using medical data collected and recorded by rheumatologists from patient characteristics, disease courses, laboratory data, and medication data. Our partners from the medicine side are helping to collect and access existing data. The partners in MaD-Lab carry out Machine learning and data analytics approaches on them to find a remission or development of the prognostic model.
BioPsyKit – An Open-Source Python Package for the Analysis of Biopsychological Data
(Own Funds)
URL: https://biopsykit.readthedocs.io/en/latest/index.html
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.
Empatho-Kinaesthetic Sensor Technology
(Third Party Funds Group – Overall project)
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://empkins.de/
EmpkinS iRTG - EmpkinS integrated Research Training Group
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich / Integriertes Graduiertenkolleg (SFB / GRK)
URL: https://www.empkins.de/
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 researchers are supervised by two project leaders.
Sensorbasierte Bewegungs- und Schlafanalyse beim Parkinson-Syndrom
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
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 and sleep patterns are then evalulated in correlation to movement, cardiovascular and sleep phase regulation disorders.
Empathokinästhetische Sensorik für Biofeedback bei depressiven Patienten
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
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 with the help of currently available technologies (e.g., RGB and depth cameras, wired EMG, established emotion recognition software) and use them as input parameters for new machine learning models to automatically detect depression-associated affect expressions. Secondly, a fully automated biofeedback paradigm is to be implemented and validated using the project results available up to that point. More ways of real-time feedback of depression-relevant kinaesthesia are investigated. Thirdly, we will research possibilities of mobile use of the biofeedback approach developed up to then.
Erforschung der posturalen Kontrolle basierend auf sensomotorisch erweiterten muskuloskelettalen Menschmodellen
(Third Party Funds Group – Sub project)
Term: 1. July 2021 - 30. June 2025
Funding source: DFG / Sonderforschungsbereich (SFB)
URL: https://www.empkins.de/
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 are to be transferred to the developed models as soon as possible.
Holistic customer-oriented service optimization for fleet availability
(Third Party Funds Single)
Funding source: Industrie, andere Förderorganisation
Symptom detection and prediction using inertial sensor-based gait analysis
(Own Funds)
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 and estimating gait parameters. This project is dedicated to detecting and possibly predicting the motor symptoms of Parkinson's disease such as Bradykinesia, Dyskinesia, and the freeze of gait. This also includes the improvement of the existing gait analysis algorithms to fit the parkinsonian gait more accurately, which is the basis of symptom detection.
Das Okulomotor Test System in der Virtuellen Realität -- Telemedizinische Detektion von Gehirnerschütterungen in VR
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
URL: https://www.mad.tf.fau.de/research/projects/vr-ots/
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.
Tracking-based museum visitor research
(Third Party Funds Single)
Funding source: Industrie
Video-based Re-Identification for Animals
(Own Funds)
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.
The automated analysis of video footage from surveillance cameras is a possibility to evaluate activity/inactivity and stereotypy of the animals as well as their enclosure use over long periods of time and thus allows an objective assessment. In the project's next phase, further behavior analysis will be added (for example, secondary behavior such as feeding, playful behavior, or interaction). The most challenging step for such a system is the re-identification (reID) of individual animals in every camera perspective
Individuelle Sportschuhempfehlung durch maschinelles Lernen
(Non-FAU Project)
Even though sport shoe models can differ in properties (fit, cushioning, bending stiffness etc.) the products come out of mass production and are made for generic groups of athletes. Humans are highly individual and research has shown that athletes are responding individually to certain shoe characteristics. Goal of this research is to create predictions for individual athletes to benefit from the right choice of shoes - be it through increased comfort, better performance or injury prevention.
Mobility in atypical parkinsonism: a randomized trial of physiotherapy
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
URL: https://gepris.dfg.de/gepris/projekt/438496663?context=projekt&task=showDetail&id=438496663&
Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie undRückfallvermeidung am Beispiel von Brustkrebs - DigiOnko
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie und Rückfallvermeidung am Beispiel von Brustkrebs
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
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 patient compliance by improving information and patient integration through electronic health applications. Thus, a reduction in mortality and an improvement in quality of life can be achieved. Within the framework of this project, digital health programmes are going to be created that support and complement health care. The project aims to provide better and faster access to new diagnostic and therapeutic procedures in mainstream oncology care, to implement eHealth models for more efficient and effective cancer care, and to improve capacity for patients in oncologcal therapy in times of crisis (such as the SARS-CoV-2 pandemic). The Chair of Health Management is conducting the health economic evaluation and analysing the extent to which digitalisation can contribute to a reduction in the costs of treatment and care as well as to an improvement in the quality of life of breast cancer patients.
Activity Recognition using IMU Sensors integrated in Hearing Aids
(Third Party Funds Single)
Funding source: Industrie
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 processing algorithms to enhance speech intelligibility and audio quality. Furthermore, inertial sensors may be used to analyze the user’s movements and physical activity. Hearing aid amplification settings may be adapted according to the current activity. Moreover, given the user's explicit consent, activity recognition enables a long-term tracking of the user’s daily activity status.
The objective of this project is to investigate automatic activity recognition based on inertial sensor data. Therefore, data of different activities will be recorded using the IMU sensor integrated in the hearing aids. The hearing aids are provided by the cooperation partner WS Audiology. Machine learning algorithms will be developed to automatically classify different activity patterns.
Biomarkers for immunotherapy in multiple sclerosis patients
(Third Party Funds Single)
Funding source: andere Förderorganisation
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 the disease in the form of relapses.
Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement
(Third Party Funds Single)
Funding source: Europäische Union (EU)
URL: http://www.mobilise-d.eu/
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: to deliver a valid solution (consisting of sensor, algorithms, data analytics, outcomes) for real-world digital mobility assessment; to validate digital outcomes in predicting clinical outcome in chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture recovery and congestive heart failure; and, to obtain key regulatory and health stakeholder approval for digital mobility assessment. The objectives address the call directly by linking digital assessment of mobility to clinical endpoints to support regulatory acceptance and clinical practice. MOBILISE-D consists of 35 partners from 13 countries with long, successful collaboration, combining the requisite expertise to address the technical and clinical challenges. To achieve the objectives, partners will jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes can successfully predict relevant clinical outcomes and provide a better, safer and quicker way to arrive at the development of innovative medicines. MOBILISE-D's results will directly facilitate drug development, and establish the roadmap for clinical implementation of new, complementary tools to identify, stratify and monitor disability, so enabling widespread, cost-effective access to optimal clinical mobility management through personalised healthcare.
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