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.
a. https://www.gesundheitsindustrie-bw.de/en/article/news/ehr-and-phr-digital-records-in-the-german-healthcare-system
b. https://www.gematik.de/anwendungen/e-patientenakte/#
c. https://refinio.net/software.html
We conduct theoretical and applied research for wearable computing systems and machine learning algorithms for engineering applications at the intersection of sports and health care. Our motivation is generating a positive impact on human wellbeing, be it through increasing performance, maintaining health, improving rehabilitation, or monitoring disease.
Research projects
Current projects
Maschinelles Lernen für Haltbarkeits- und Absatzprognosen und Bestimmung der Authentizität
(Third Party Funds Group – Sub project)
Term: 1. April 2022 - 31. December 2023
Funding source: Bayerische Forschungsstiftung
URL: https://www.bayfor.org/de/unsere-netzwerke/bayerische-forschungsverbuende/forschungsverbuende/project/shield/teilprojekt-3-masch
Ziel dieses Teilprojekts ist es, datengetriebene Vorhersagen über Absatz und Haltbarkeit der Produkte unserer Industriepartner zu treffen, um Nahrungsmittelverluste zu reduzieren, während der Umsatz erhöht wird. Eine besondere Herausforderung von frischen Lebensmitteln stellt ihre kurze Haltbarkeit da, welche eine besonders präzise Absatzprognose notwendig macht. Wir adressieren dieses Problem mit einem auf Bayes‘schen maschinellen Lernen basierenden Ansatz. Bayes‘sche Methoden liefern zusätzlich zu einer punktuellen Vorhersage (z. B. über den Absatz) auch ein Maß über die Unsicherheit dieser Vorhersage.
Machine Learning for CT-Module 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.
Trusted Ecosystem of Applied Medical Data eXchange; Teilvorhaben: FAU@TEAM-X
(Third Party Funds Group – Sub project)
Term: 1. January 2022 - 31. December 2024
Funding source: Bundesministerium für Wirtschaft und Technologie (BMWi)
Adaptive AI Systems in Sport
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
The digitization of the sports sector leads to the individualization of products and services for everyday athletes. To be able to ensure this, artificial intelligence approaches are needed to know how to create personalized value for the athlete/consumer from large heterogeneous data sets.
An application example for this is the home training sector, which is gaining importance especially due to the effects of the Corona pandemic. Commercial platforms offer initial approaches to the use of immersive media but fail to generate individualization of content by analyzing heterogeneous data sources for the user.
The project will therefore investigate mechanisms for user engagement and motivation. Based on this, a comprehensive adaptive AI system for predicting individual goal achievement will be developed and fused with additional data sources. Based on the predictions, a framework for designing stimulus-driven real-time systems for individualizing immersive user interfaces will be defined. The integration of the resulting subsystems into a high-fidelity prototype enables the transfer to further application domains.
dhip campus-bavarian aim
(Third Party Funds Group – Overall project)
Funding source: Industrie
Personalized prediction of medications responses in patients with rheumatoid arthritis using Machine Learning algorithms
(Third Party Funds Group – Sub project)
Term: 1. September 2021 - 30. August 2024
Funding source: Industrie
There is a wide range of medications for RA patients, Clinical trials and real-time experience demonstrate that sometimes these treatments have adverse effects, for better benefits and later minimizing the damage, we should predict the response for each person.
This project aims to collect medical data on rheumatology arthritis, select the best factors and identify important clinical features associated with remission and then create a model to predict remissions in patients and prediction of treatment response and course of activity for each patient using machine learning methods. This project could help in preventing wrong prescriptions and time-wasting before disease progression.
We want to reach the aim by using medical data collected and recorded by rheumatologists from patient characteristics, disease courses, laboratory data, and medication data. Our partners from the medicine side are helping to collect and access existing data. The partners in MaD-Lab carry out Machine learning and data analytics approaches on them to find a remission or development of the prognostic model.
Empatho-Kinaesthetic Sensor Technology
(Third Party Funds Group – Overall project)
Funding source: DFG / Sonderforschungsbereich / Transregio (SFB / TRR)
URL: https://empkins.de/
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.
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.
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.
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.
Holistic customer-oriented service optimization for fleet availability
(Third Party Funds Single)
Funding source: Industrie, andere Förderorganisation
Symptom detection and prediction using inertial sensor-based gait analysis
(Own Funds)
Parkinson's disease after Alzheimer's is the second most common neurodegenerative disease which mainly affects the patient's mobility and produces gait insecurity and impairment. As patients experience various, asymmetrical and heterogeneous gait characteristics, personalized medication should be at the center of attention in controlling motor complications in Parkinson's patients. potentially, inertial measurement units (IMUs) can be utilized for long-term observation of the disease progress and estimating gait parameters. This project is dedicated to detecting and possibly predicting the motor symptoms of Parkinson's disease such as Bradykinesia, Dyskinesia, and the freeze of gait. This also includes the improvement of the existing gait analysis algorithms to fit the parkinsonian gait more accurately, which is the basis of symptom detection.
Das Okulomotor Test System in der Virtuellen Realität -- Telemedizinische Detektion von Gehirnerschütterungen in VR
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
URL: https://www.mad.tf.fau.de/research/projects/vr-ots/
The aim of the project is to develop a rapid automated diagnosis (using artificial intelligence) of a fusion disorder in cases of e.g. concussion by measuring the oculomotor function (eye-tracking) for three-dimensional stimuli.
Onlinebetrug verursachte 2019 weltweit ca. 56 Mrd. $ Schaden. Die Methoden dafür werden stetig komplexer und damit schwerer aufzudecken. Im Vorhaben sollen digitale Betrugsidentitäten und Betrugsformen durch KI in Echtzeit erkannt werden
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie (StMWi) (seit 2018)
Onlinebetrug verursachte 2019 weltweit ca. 56 Mrd. $ Schaden. Die Methoden dafür werden stetig komplexer und damit schwerer aufzudecken. Im Vorhaben sollen digitale Betrugsidentitäten und Betrugsformen durch KI in Echtzeit erkannt werden.
Tracking-based museum visitor research
(Third Party Funds Single)
Funding source: Industrie
Individuelle Sportschuhempfehlung durch maschinelles Lernen
(Non-FAU Project)
Even though sport shoe models can differ in properties (fit, cushioning, bending stiffness etc.) the products come out of mass production and are made for generic groups of athletes. Humans are highly individual and research has shown that athletes are responding individually to certain shoe characteristics. Goal of this research is to create predictions for individual athletes to benefit from the right choice of shoes - be it through increased comfort, better performance or injury prevention.
Mobility in atypical parkinsonism: a randomized trial of physiotherapy
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
URL: https://gepris.dfg.de/gepris/projekt/438496663?context=projekt&task=showDetail&id=438496663&
Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie undRückfallvermeidung am Beispiel von Brustkrebs - DigiOnko
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Integratives Konzept zur personalisierten Präzisionsmedizin in Prävention, Früh-Erkennung, Therapie und Rückfallvermeidung am Beispiel von Brustkrebs
(Third Party Funds Single)
Funding source: Bayerisches Staatsministerium für Gesundheit und Pflege, StMGP (seit 2018)
Breast cancer is one of the leading causes of death in the field of oncology in Germany. For the successful care and treatment of patients with breast cancer, a high level of information for those affected is essential in order to achieve a high level of compliance with the established structures and therapies. On the one hand, the digitalisation of medicine offers the opportunity to develop new technologies that increase the efficiency of medical care. On the other hand, it can also strengthen patient compliance by improving information and patient integration through electronic health applications. Thus, a reduction in mortality and an improvement in quality of life can be achieved. Within the framework of this project, digital health programmes are going to be created that support and complement health care. The project aims to provide better and faster access to new diagnostic and therapeutic procedures in mainstream oncology care, to implement eHealth models for more efficient and effective cancer care, and to improve capacity for patients in oncologcal therapy in times of crisis (such as the SARS-CoV-2 pandemic). The Chair of Health Management is conducting the health economic evaluation and analysing the extent to which digitalisation can contribute to a reduction in the costs of treatment and care as well as to an improvement in the quality of life of breast cancer patients.
Activity Recognition using IMU Sensors integrated in Hearing Aids
(Third Party Funds Single)
Funding source: Industrie
The hearing aid of the future will be more than just an amplifying device. It may be used as fitness tracker to capture the user’s movements and activity level. Furthermore, it may be used as home monitoring device assessing the user’s vital parameters, tracking the user’s activity status or detecting falls. Hearing aids are becoming more complex and most modern hearing aids are already equipped with additional sensors such as inertial sensors. Acceleration signals are analyzed with signal processing algorithms to enhance speech intelligibility and audio quality. Furthermore, inertial sensors may be used to analyze the user’s movements and physical activity. Hearing aid amplification settings may be adapted according to the current activity. Moreover, given the user's explicit consent, activity recognition enables a long-term tracking of the user’s daily activity status.
The objective of this project is to investigate automatic activity recognition based on inertial sensor data. Therefore, data of different activities will be recorded using the IMU sensor integrated in the hearing aids. The hearing aids are provided by the cooperation partner WS Audiology. Machine learning algorithms will be developed to automatically classify different activity patterns.
Federated Machine Learning for Patient-Centered Electronic Health Records
(Third Party Funds Single)
Funding source: Deutscher Akademischer Austauschdienst (DAAD)
URL: https://www.mad.tf.fau.de/research/projects/federated-machine-learning-for-patient-centered-electronic-health-records/
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.
a. https://www.gesundheitsindustrie-bw.de/en/article/news/ehr-and-phr-digital-records-in-the-german-healthcare-system
b. https://www.gematik.de/anwendungen/e-patientenakte/#
c. https://refinio.net/software.html
SMART Start: Smarte Sensorik in der Schwangerschaft - Ein integratives Konzept zur digitalen, präventiven Versorgung schwangerer Frauen
(Third Party Funds Single)
Funding source: Bundesministerium für Gesundheit (BMG)
Sensorische Anwendungen finden heutzutage durch moderne Technologien (v.a. Smartphone/Smart-Watch vermittelt) vielfach Einzug in den Alltag. In diesem Zuge stellt sich die Frage, inwieweit auch sensorische Messungen der regulären Schwangeren-Vorsorge (Herzfrequenz, Blutdruck, Sonografie und Kardiotokografie), die dem Standard nach in der Hand des Arztes oder der Ärztin liegen, in den Smart-Home Bereich transferiert werden und valide Ergebnisse liefern, sowie zukünftig die Klinik-besuche schwangerer Frauen reduzieren bzw. spezifizieren können. Im Fokus der Fragestellung dieses Projekts steht die klinische Usability, die gesellschaftliche Akzeptanz, die Compliance durch die betroffenen Akteure und die Weiterentwicklung dieser sensorischen Techniken im häuslichen Bereich sowie damit assoziierte ethisch/medizinrechtliche Themen.
Ziel des Projektes ist, die Vorsorge für schwangere Frauen zu optimieren und zu vereinfachen, indem sowohl bewährte als auch innovative Sensorik in die Heim-Versorgung überführt und mit künstlicher Intelligenz und maschinellem Lernen analysiert wird. In diesem Projekt werden direkte Anwendungsmöglichkeiten zur Implementierung der Smart-Sensorik geschaffen, welche die optimierte Gesundheitsbetreuung durch die Ärztin oder den Arzt, aber auch die eigene Kontrolle und Optimierung der metabolischen Aktivität durch die schwangeren Frauen ermöglicht. Als Zielgruppe sind schwangere Frauen und deren Partner/innen angesprochen, die offen sind für die gesundheitsbezogene Anwendung moderner, digitaler Medien (Smartphone, Smart-Watch etc.).
Biomarkers for immunotherapy in multiple sclerosis patients
(Third Party Funds Single)
Funding source: andere Förderorganisation
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) that is most common in young adult. The disease is due to an autoimmune reaction in which immune cells, which attack foreign pathogens, damage the body's own tissue. This project aims at identifying biomarkers in the form of gait characteristics in patients with multiple sclerosis which can be used both for the assessment of the current state of the disease and for the prediction of the further course of the disease in the form of relapses.
Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement
(Third Party Funds Single)
Funding source: Europäische Union (EU)
URL: http://www.mobilise-d.eu/
Optimal treatment of the impaired mobility resulting from ageing and chronic disease is one of the 21st century's greatest challenges facing patients, society, governments, healthcare services, and science. New interventions are a key focus. However, to accelerate their development, we need better ways to detect and measure mobility loss. Digital technology, including body worn sensors, has the potential to revolutionise mobility assessment. The overarching objectives of MOBILISE-D are threefold: to deliver a valid solution (consisting of sensor, algorithms, data analytics, outcomes) for real-world digital mobility assessment; to validate digital outcomes in predicting clinical outcome in chronic obstructive pulmonary disease, Parkinson’s disease, multiple sclerosis, proximal femoral fracture recovery and congestive heart failure; and, to obtain key regulatory and health stakeholder approval for digital mobility assessment. The objectives address the call directly by linking digital assessment of mobility to clinical endpoints to support regulatory acceptance and clinical practice. MOBILISE-D consists of 35 partners from 13 countries with long, successful collaboration, combining the requisite expertise to address the technical and clinical challenges. To achieve the objectives, partners will jointly develop and implement a digital mobility assessment solution to demonstrate that real-world digital mobility outcomes can successfully predict relevant clinical outcomes and provide a better, safer and quicker way to arrive at the development of innovative medicines. MOBILISE-D's results will directly facilitate drug development, and establish the roadmap for clinical implementation of new, complementary tools to identify, stratify and monitor disability, so enabling widespread, cost-effective access to optimal clinical mobility management through personalised healthcare.
Machine Learning for Predictive Analytics
(Third Party Funds Single)
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:
Performance Analysis in Team Sports
(Third Party Funds Single)
Funding source: Industrie
Performance Analysis in team sports is an emerging field in computer science. In Europe's leagues, a large amount of data is recorded during the season. Based on methods of machine learning and signal processing an automated, fast and accurate analysis of matches is possible.
In this project, the performance of a single player and the behavior of the whole team (e.g. tactics) is calculated based on position and inertial sensor data.
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2020
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