Prof. Dr. Björn Eskofier

Department of Artificial Intelligence in Biomedical Engineering (AIBE)

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

  • Several gait analysis projects are concerned with determining disease progression
  • Disease classification and fall-risk using wearable IMU-based sensors
  • Projects in the biomedical analysis field extract meaning from human body signals like the heart

Current projects

  • Smart Wound Dressing incorporating Dye-based Sensors Monitoring of O2, pH and CO2 under the wound dressing and smart algorithms to assess the wound healing process

    (Third Party Funds Single)

    Term: 1. September 2023 - 31. July 2026
    Funding source: Bayerische Forschungsstiftung

    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 wounds, which can monitor the energy-metabolic tissue and wound healing status individually for each patient and online by integrating fluorescent dye-based oxygen, pH and CO2 sensors. 

  • 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.
  • Digital health application for the therapy of incontinence patients

    (Third Party Funds Single)

    Term: since 1. January 2023
    Funding source: Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

    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. 

  • 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.

  • DIAMond - diabetes type 1 management with personalized recommendation using data science

    (Third Party Funds Single)

    Term: 1. September 2022 - 20. January 2025
    Funding source: Deutscher Akademischer Austauschdienst (DAAD)

    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 type 1 diabetes using data science and machine learning to gain insights into the problem of hypoglycemia. Data from the DIAcamp study is used to advance personalized treatment recommendations. In the DIAcamp study, children participated for one week. They were equipped with a continuous glucose sensor and a wearable device for monitoring heart rate, accelerometry, and further physiological parameters during their participation. Physicians and carers from the DIAcamp study documented insulin doses, carbohydrate intake, and time and type of activity. Within the DIAmond project, novel machine learning algorithms will determine the probability of hypoglycemia. Exploratory analysis of the physiological time series will result in the most predictive features, building the base for personalized treatment recommendations. 

    This project is a joint project with the Department of Computer Science, ETH Zurich, Switzerland.

  • 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.

  • 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. 

  • 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.

  • 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.

  • 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)
  • 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.

  • dhip campus-bavarian aim

    (Third Party Funds Group – Overall project)

    Term: 1. October 2021 - 30. September 2027
    Funding source: Industrie
  • 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.

  • 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.

  • 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.

  • 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.
  • 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.

  • 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.

  • 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.

  • 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.

  • Holistic customer-oriented service optimization for fleet availability

    (Third Party Funds Single)

    Term: 1. June 2021 - 31. May 2024
    Funding source: Industrie, andere Förderorganisation
  • 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.

  • 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)
  • 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.

Recent publications

2024

2023

2022

2021

2020

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