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
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.
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.
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.
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.
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.
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.
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.
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. March 2025 Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
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.
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.
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.
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.
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.
Meier, J.J., Weigand, C., Bauer, J., & Eskofier, B. (2024). MANAGEMENT BY DIGITAL RESPONSIBILITY IN HEALTH ORGANIZATIONS. In Ajith Abraham, Guo Chao Peng, Pedro Isaias, Pedro Isaias (Eds.), Proceedings of the International Conferences on Big Data Analytics, Data Mining and Computational Intelligence 2024, BigDaCI 2024; Connected Smart Cities 2024, CSC 2024; and e-Health 2024, EH 2024 (pp. 233-238). Budapest, HUN: IADIS.
Schrupp, B., Klede, K., Raab, R., & Eskofier, B. (2024). Simulation and Detection of Healthcare Fraud in German Inpatient Claims Data. In Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (Eds.), Computational Science -- ICCS 2024 (pp. 239--246). Cham: Springer.
Gabler, E., Nissen, M., Altstidl, T.R., Titzmann, A., Packhäuser, K., Maier, A.,... Leutheuser, H. (2023). Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1-4). Sydney, NSW, AU: Institute of Electrical and Electronics Engineers Inc..
Klede, K., Altstidl, T.R., Zanca, D., & Eskofier, B. (2023). p-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison. In A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine (Eds.), Advances in Neural Information Processing Systems 36 (pp. 27113–27128). New Orleans, US: Curran Associates, Inc..
Krauß, D., Richer, R., Albrecht, N.C., Küderle, A., Abel, L., Leutheuser, H.,... Eskofier, B. (2023, October). Contactless Heart Rate Estimation using a 61 GHz Continuous-Wave Radar. Poster presentation at IEEE-EMBS International Conference on Body Sensor Networks: Sensor and Systems for Digital Health, MIT Media Lab, Boston MA, US.
Lennartz, R., Khassetarash, A., Spyrou, E., Hallihan, A., Eskofier, B., & Nigg, B. (2023, August). The Influence of Protective Equipment on Performance in Ice Hockey. Poster presentation at XXIX Conference of the International Society of Biomechanics (ISB), Fukuoka, JP.
Moradi, H., Hannink, J., Stallforth, S., Gladow, T., Ringbauer, S., Mayr, M.,... Eskofier, B. (2023). Monitoring medication optimization in patients with Parkinson’s disease. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Sydney, AU.
Zugarini, A., Röthenbacher, T., Klede, K., Ernandes, M., Eskofier, B., & Zanca, D. (2023). Die Rätselrevolution: Automated German Crossword Solving. In Federico Boschetti, Gianluca E. Lebani, Bernardo Magnini, Nicole Novielli (Eds.), Proceedings of the 9th Italian Conference on Computational Linguistics. Venice, IT.
Cakici, A., Oßwald, M., Souza de Oliveira, D., Braun, D., Simpetru, R., Kinfe, T.M.,... Del Vecchio, A. (2022). A Generalized Framework for the Study of Spinal Motor Neurons Controlling the Human Hand During Dynamic Movements. In Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 (pp. 4115-4118). Scottish Event Campus, Glasgow, GB: Institute of Electrical and Electronics Engineers Inc..
Hecht, D., Pfahler, T., Ullmann, I., Altstidl, T.R., Amer, N., Jin, Y.,... Vossiek, M. (2022). In Vivo Skin-Type Classification Using Millimeter-Wave Near-Field Probe Spectroscopy. In Institute of Electrical and Electronics Engineers (IEEE) (Eds.), 2022 52nd European Microwave Conference. Milan, IT: Milan: Institute of Electrical and Electronics Engineers (IEEE).
Müller, V., Richer, R., Henrich, L., Berger, L., Gelardi, A., Jäger, K.,... Rohleder, N. (2022). The Stroop Competition: A Social-Evaluative Stroop Test for Acute Stress Induction. In 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) (Eds.), Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). Ioannina, GR.
Rohleder, N., Richer, R., Koch, V., Küderle, A., Müller, V., Wirth, V.,... Eskofier, B. (2022, July). Effect of Acute Psychosocial Stress on Body Movements. Paper presentation at 79th Annual Scientific Meeting of the American Psychosomatic Society, Long Beach, CA, US.
Dib, W., Ghanem, K., Nedil, M., Ababou, A., & Eskofier, B. (2021). Identification of individuals through a new Gait Recognition Method. In 2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021 - Proceedings (pp. 1813-1814). Singapore, SG: Institute of Electrical and Electronics Engineers Inc..
Dumbach, P., Liu, R., Jalowski, M., & Eskofier, B. (2021). The Adoption Of Artificial Intelligence In SMEs - A Cross-National Comparison In German And Chinese Healthcare. In Forbig P., Hinkelmann K., Kirikova M., Lantow B., Møller C., Morichetta A., Plebani P., Re B., Sandkuhl K., Seigerroth U. (Eds.), Joint Proceedings of the BIR 2021 Workshops and Doctoral Consortium co-located with 20th International Conference on Perspectives in Business Informatics Research (BIR 2021) (pp. 84 - 98). Vienna, Austria (fully-virtual conference), AT: CEUR Workshop Proceedings.
Maier, J., Nitschke, M., Choi, J.H., Gold, G., Fahrig, R., Eskofier, B., & Maier, A. (2021). Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 336-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Schleicher, R., Nitschke, M., Martschinke, J., Stamminger, M., Eskofier, B., Klucken, J., & Koelewijn, A. (2021). BASH: Biomechanical Animated Skinned Human for Visualization of Kinematics and Muscle Activity. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP (pp. 25-36). Online.
Wehbi, M., Hamann, T., Barth, J., Kaempf, P., Zanca, D., & Eskofier, B. (2021). Towards an IMU-based Pen Online Handwriting Recognizer. In Proceedings of the International Conference on Document Analysis and Recognition ICDAR 2021 (pp. 289-303). Lausanne, CH: Springer Link.
Fischer, S., Ullrich, M., Küderle, A., Gaßner, H., Klucken, J., Eskofier, B., & Kluge, F. (2020). Automatic clinical gait test detection from inertial sensor data. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 789 - 792). Montreal, CA.
Köferl, F., Link, J., & Eskofier, B. (2020). Application of SORT on Multi-Object Tracking and Segmentation. In Proceedings of the Conference on Computer Vision and Pattern Recognition;
5th BMTT MOTChallenge Workshop: Multi-Object Tracking and Segmentation. Seattle, WA, USA (Virtual).
Maier, J., Nitschke, M., Choi, J.-H., Gold, G., Fahrig, R., Eskofier, B., & Maier, A. (2020). Inertial Measurements for Motion Compensation in Weight-Bearing Cone-Beam CT of the Knee. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. 14-23).
Mehringer, W., Wirth, M., Gradl, S., Durner, L., Ring, M., Laudanski, A.F.,... Michelson, G. (2020). An Image-Based Method for Measuring Strabismus in Virtual Reality. In IEEE (Eds.), 2020 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) (pp. 5-12). Recife, BR.
Rüthlein, M., Köferl, F., Mehringer, W., & Eskofier, B. (2020). Interactive Segmentation of RGB-D Indoor Scenes using Deep Learning. In Proceedings of the International Conference on Machine Learning;
2nd ICML 2020 Workshop on Human in the Loop Learning. Virtual Conference.
Wirth, M., Gradl, S., Mehringer, W., Kulpa, R., Rupprecht, H., Poimann, D.,... Eskofier, B. (2020). Assessing Personality Traits of Team Athletes in Virtual Reality. In Proceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020 (pp. 101-108). Atlanta, GA, US: Institute of Electrical and Electronics Engineers Inc..
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
Center for AI in Medicine
(Own Funds)
Federated virtual twins for privacy-preserving personalised outcome prediction of type 2 diabetes treatment
(Third Party Funds Single)
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)
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)
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)
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)
Term: 1. January 2023 - 31. December 2027
Funding source: Europäische Union (EU)
URL: https://www.tefhealth.eu/
Digital health application for the therapy of incontinence patients
(Third Party Funds Single)
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)
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)
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)
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.
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. March 2025
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
dhip campus-bavarian aim
(Third Party Funds Group – Overall project)
Funding source: Industrie
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.
2024
2023
2022
2021
2020
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