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

  • Digitalisierung und Edition der Prozessakten der Nürnberger Nachfolgeprozesse

    (Third Party Funds Single)

    Term: 15. November 2024 - 31. December 2026
    Funding source: andere Förderorganisation
  • Center for AI in Medicine

    (Own Funds)

    Term: since 1. May 2024
  • Federated virtual twins for privacy-preserving personalised outcome prediction of type 2 diabetes treatment

    (Third Party Funds Single)

    Term: 1. January 2024 - 31. December 2028
    Funding source: EU - 9. Rahmenprogramm - Horizon Europe
    URL: https://daibetes.eu/
    Virtual twins have the potential to be used as prognostic tools in precision medicine to support individualized disease management. However, training these models requires large volumes of data from various sources, which is challenging due to privacy regulations like the General Data Protection Regulation (GDPR). Recently, privacy-preserving computational methods, such as federated learning, have emerged, offering a way to utilize extensive data effectively while protecting sensitive patient information.In dAIbetes, our main medical objective is to provide personalized predictions of treatment outcomes for type 2 diabetes, a condition affecting 1 in 10 adults globally and leading to annual costs of approximately 893 billion EUR. Although healthcare professionals are improving at addressing diabetes risk factors like diet and exercise, there are currently no guidelines for predicting treatment outcomes tailored to individual patients.dAIbetes brings together advaned expertise in federated learning, artificial intelligence, cybersecurity, diabetes data standardization, clinical validation, as well as in legal and ethical evaluation of applying advanced federated machine learning to personalized medicine. 13 Partners from 13 European countries and the US will jointly implement the project which is structured into 9 Work Packages (WP1-WP9). At FAU, we are working on WP3, i.e., the development of virtual twin apps for training of virtual twin models that will use data from type 2 diabetes patients.
  • Automatisierte Kuratierung von in-vivo erfassten Zeitreihen

    (Third Party Funds Single)

    Term: 1. January 2024 - 31. December 2026
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
  • 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. 
  • Privacy-preserving analysis of distributed medical data

    (Own Funds)

    Term: since 1. July 2023
    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 of the technical solutions is privacy-enhancing technologies (PETs), such as secure multi-party computation and (local) differential privacy, which safeguard individuals' privacy while enabling the statistical analysis of aggregate data.

  • 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. 
  • 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.
  • dhip campus-bavarian aim

    (Third Party Funds Group – Overall project)

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

2025

2024

2023

2022

2021

2020

Related Research Fields

Contact: