Prof. Claudio Castellini

Chair of Medical Robotics / Department Artificial Intelligence in Biomedical Engineering (AIBE)

I am a researcher in medical robotics, focussing on rehabilitation and assistive robotics, human-machine interfaces and interaction and applied machine learning. Currently, I hold the Chair in Medical Robotics at FAU, and keep a part-time position as team leader and senior researcher at the Institute of Robotics and Mechatronics of the German Aerospace Center. My main interest lies in the proper design and translation of integrated rehabilitation and assistive solutions for patients of musculoskeletal degenerative conditions, sufferers of limb deficiency and elderly people needing semi-autonomous care.

Research Projects

  • Investigating advanced sensors and sensing modalities for intent detection
  • Investigating techniques to provide sensory feedback to users of rehabilitation and assistive devices
  • Strategies to shape the interaction and user experience
  • Theoretical machine learning and psychology – two sides of the same coin!

  • Unsupervised Network Medicine for Longitudinal Omics Data

    (FAU Funds)

    Term: since 15. January 2022

    Over the last years, large amounts of molecular profiling data (also called “omics data”) have become available. This has raised hopes to identify so-called disease modules, i.e., sets of functionally related molecules constituting candidate disease mechanisms. However, omics data tend to be overdetermined and noisy; and modules identified via purely statistical means are hence often unstable and functionally uninformative. Hence, network-based disease module mining methods (DMMMs) project omics data onto biological networks such as protein-protein interaction (PPI) networks, gene regulatory networks (GRNs), or microbial interaction networks (MINs). Subsequently, network algorithms are used to identify disease modules consisting of small subnetworks. This dramatically decreases the size of the search space and prioritizes disease modules consisting of functionally related molecules, positively affecting both stability and functional relevance of the discovered modules.

    However, to the best of our knowledge, all existing DMMMs are subject to at least one of the following two limitations: Firstly, existing DMMMs are typically supervised, in the sense that they try to find subnetworks explaining differences in the omics data between predefined case and control patients or pre-defined disease subtypes. This is potentially problematic, because it implies that existing DMMMs are biased by our current disease ontologies, which are mostly symptom- or organ-based and therefore often too coarse-grained. For instance, around 95 % of all patients with hypertension are diagnosed with so-called “essential hypertension” (code BA00.Z in the ICD-11 disease ontology), meaning that the cause of the hypertension is unknown. In fact, there are probably several disjoint molecular mechanisms causing “essential hypertension”, and the same holds true for many other complex diseases such as Alzheimer’s disease, multiple sclerosis, and Crohn’s disease. Supervised DMMMs which take existing disease definitions for granted hence risk overlooking the molecular mechanisms causing mechanistically distinct subtypes.

    Secondly, most existing DMMMs are designed for static omics data and do not support longitudinal data where the patients’ molecular profiles are observed over time. Existing analysis frameworks for longitudinal omics data largely use purely statistical means. Consequently, network medicine approaches for time series data are needed.

    To the best of our knowledge, there are only three DMMMs which, in part, overcome these limitations: BiCoN and GrandForest allow unsupervised disease module mining but do not support longitudinal omics data. TiCoNE supports longitudinal data but requires predefined case vs. control or subtype annotations as input. There is hence an unmet need for unsupervised DMMMs for longitudinal omics data. Developing such methods is the main objective of the proposed project.

  • Empatho-Kinaesthetic Sensor Technology

    (Third Party Funds Group – Overall project)

    Term: 1. July 2021 - 30. June 2025
    Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
    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.
  • Holistic customer-oriented service optimization for fleet availability

    (Third Party Funds Single)

    Term: 1. June 2021 - 31. May 2024
    Funding source: Industrie, andere Förderorganisation
  • Das Okulomotor Test System in der Virtuellen Realität -- Telemedizinische Detektion von Gehirnerschütterungen in VR

    (Third Party Funds Single)

    Term: 1. April 2021 - 31. December 2023
    Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)

    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)

    Term: 15. February 2021 - 31. December 2023
    Funding source: Industrie
  • Mobility in atypical parkinsonism: a randomized trial of physiotherapy

    (Third Party Funds Single)

    Term: since 1. November 2020
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
    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.
  • 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.

  • 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. 

  • Federated Machine Learning for Patient-Centered Electronic Health Records

    (Third Party Funds Single)

    Term: 1. April 2020 - 30. September 2022
    Funding source: Deutscher Akademischer Austauschdienst (DAAD)

    Electronic health records (EHR) are commonly institution-specific, provided by hospitals, insurance companies, or other institutions to fulfill their own objectives, thus, causing stored health information to be isolated, fragmented and duplicated across providers. Consequently, patients may lack complete access to their medical histories. As a solution, countries such as Denmark and Israel have long adopted nationwide EHR for their health care systemsa, in which the health information is well managed and digitally connected to avoid duplicate records and improve the quality and co-effectiveness of medical care as well as patient safety. In Germany, the “Appointment Service and Supply Act” adopted on 14th March 2019, requires the German statutory health insurance to provide EHR for all insured persons from 1st January 2021 onwardsb. As specified by German Health Care Information Technology Infrastructure in accordance with section 291a SGB V, the new EHR should store complete medical histories of patients such as previous diagnoses, therapeutic decisions, treatment reports and self-measurement values. Among other benefits, patients should have power to select freely between providers, hold data sovereignty for their EHR, and withdraw access rights at any time.

    In line with those guidelines, OnePatientc is a patient-centered EHR system that stores data locally under the sovereignty of individual device owners, thereby enabling patients to take control of their health information, provide offline access to medical data, ensure privacy management and to avoid a single point of failure. The OnePatient EHR system can be provisioned on any of the patients’ devices; therefore, patients technically own their medical data while the device and software manage it. On the one hand, these developments simplify the technical and organizational challenges to implement data regulations such as the General Data Protection Regulation (GDPR) of the European Union. On the other hand, the data will not only be in isolated, heterogeneous and distributed environments but also pose a new challenge to the conventional data transaction procedures employed in machine learning (ML) today [4]. The traditional procedures for acquiring big data in ML involve several parties from collecting the data, transferring it to a central data repository and fusing it to build a model, whereas the data owners may be unclear about these procedures and the model future use cases, for that reason, may violate laws such as GDPR.

    Therefore, to address these challenges, federated learning (FL) approaches can be leveraged to build ML models that can be sent to train locally–where the data is located. In this manner, only the model updates that contain anonymous results which cannot be reverse-engineered are returned to the central data repository. Leveraging FL and the account of the FL existing studies [1; 2; 3], although not focusing on the emerging EHR systems’ architecture like OnePatient, we aim to attain four objectives. The first is to investigate and design novel FL frameworks that enable local systems to collaboratively train a ML model that patients can benefit from without divulging their medical information to a central entity; moreover, medical practitioners will be able to access the training process of the FL frameworks to adjust the diagnostic criteria of the model, and therefore increase trust and accuracy of the model outcome. Secondly, we aim to investigate and compare the accuracy and performance of the model trained in a centralized way and the FL frameworks that will be proposed. Thirdly, to protect the data during training from potentially malicious models and participants, we aim to use countermeasures such as differential privacy and multi-party computation to ensure privacy guarantees. Finally, for proof of concept, we aim to demonstrate the effectiveness of FL frameworks using existing databases and suitable ML tasks with the data.

    1. Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. Federated learning of predictive models from federated electronic health records. Int.J.Med.Inf. 2018; 112: 59-67. 

    2. Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N., & Wachinger, C. Braintorrent: A peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731 2019; 

    3. Xu, J., & Wang, F. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 2019;

    4. Yang, Q., Liu, Y., Chen, T., & Tong, Y. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 2019; 10: 1-19. 


  • Heisenberg-Förderung / Verlängerung - 2. Phase

    (Third Party Funds Single)

    Term: 1. March 2020 - 28. February 2022
    Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
  • SMART Start: Smarte Sensorik in der Schwangerschaft - Ein integratives Konzept zur digitalen, präventiven Versorgung schwangerer Frauen

    (Third Party Funds Single)

    Term: 1. March 2020 - 30. April 2023
    Funding source: Bundesministerium für Gesundheit (BMG)
  • 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.

  • Development of neural networks and machine learning algorithms for online handwriting recognition

    (Third Party Funds Group – Sub project)

    Overall project: Development of neural networks and machine learning algorithms for online handwriting recognition
    Term: 2. September 2019 - 30. April 2022
    Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013), Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)

    The aim of this project is the development of a toolkit that is able to identify handwriting in real time. Using Stabilo Digipen with internal sensors that provides pen motion data in real time, machine learning algorithms are applied to track the pen movement on regular paper and digitize written sentences in real time.

  • Biomechanical Assessment and Simulation

    (Third Party Funds Single)

    Term: 1. August 2019 - 1. August 2022
    Funding source: Industrie

    The goal of this project is to develop data-based and knowledge-based methods for accurate analysis and simulation of human motion, focused on gait. Movement simulations are created by solving trajectory optimization problems, using an objective related to energy, a musculoskeletal model to model the body and muscle dynamics, and constraints to define the movement task. We use data-based approaches to improve musculoskeletal models and simulation accuracy. With our research, we aim to better understand human motion, and thereby improve design of wearables, such as prostheses, exoskeletons, and running shoes.

  • Entwicklung intelligenter neuronaler Netze zur Schrifterkennung

    (Third Party Funds Group – Sub project)

    Overall project: Entwicklung intelligenter neuronaler Netze zur Schrifterkennung
    Term: 1. May 2019 - 30. April 2022
    Funding source: Bayerisches Staatsministerium für Wirtschaft und Medien, Energie und Technologie (StMWIVT) (ab 10/2013)
  • Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement

    (Third Party Funds Single)

    Term: 1. April 2019 - 31. March 2024
    Funding source: Europäische Union (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.

  • MAVEHA: Automated Fetal and Neonatal Movement Assessment for Very Early Health Assessment

    (Non-FAU Project)

    Term: 1. April 2019 - 30. April 2023
  • Machine Learning for Predictive Analytics

    (Third Party Funds Single)

    Term: 1. October 2018 - 30. September 2022
    Funding source: Industrie

    The main goal of this project is to improve the overall system quality and customer satisfaction.

    In this project, we analyze IoT data (machine logs and sensory data) sent by thousands of high-end medical devices every day. The extracted information can include physical parameters and additional extracted event patterns. This data can be used to predict the failure of specific components and correlate malfunction to machine usage. As a consequence, system stability can be improved and procedures for system testing can be recommended.

    Furthermore, information from customer service data (e.g. tickets) is processed and fused with machine data to predict customer sentiment. With that, customer satisfaction can be improved via proactive service.

    Methods designed and used include:


    • Time Series Analysis (esp. mixed-typed and irregularly sampled)
    • Deep Learning
    • Process Mining
    • Data Fusion
    • Text Mining




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