Secur-e-Health - SFC Secur-e-Health – Smart Fracture Care


The goal of the Secur-e-Health project is to integrate new approaches for digital ID technologies and privacy-preserving analysis techniques in a secure system infrastructure. The Secur-e-Health system allows medical institutions of all types to collaborate together and leverage data analyses and insights. This is expected to have a significant impact on the quality of the medical predictive models, the efficiency of data-driven treatments, the acceleration of new clinical research, and the improvement of healthcare in general. The German subproject Smart Fracture Care (SFC) focuses on the therapy of musculoskeletal diseases, which are one of the most common causes of chronic pain and limited mobility in Europe. These diseases cause significant costs for the healthcare system. In addition, musculoskeletal diseases lead to work absences and productivity losses, which in turn cause economic costs. 

Musculoskeletal diseases are one of the most common causes of chronic pain and limited mobility in Europe. These diseases cause considerable costs for the health care system. In addition, musculoskeletal disorders lead to lost work time and lost productivity, which results in economic costs.  The quality of life and the living environment of people who suffer from musculoskeletal diseases is directly affected by these diseases. 

There is currently no possibility to access medical data across different facilities. There is a lack of common standards regarding security and permeability for data processing. This makes it difficult to develop Ai-based decision support and to drive continuous improvement in clinical practice.


The concept of the Smart Fracture Care project (SFC), which is located within the Secur-e-Health system as a use case, aims to improve the care of long bone trauma by digitalising clinical processes. For this purpose, information from the electronic health record (EHR), from patient reports and image data as well as operation data including operation navigation are to be integrated. Furthermore, an AI-supported care pathway is to be established, which is to be based on the patient-specific information as well as on expert knowledge.

This is being done with the aim of achieving high-quality and personalised care for the most frequently occurring fractures of the long tubular bones. Furthermore, it will help determine the best perioperative treatment and rehabilitation course for trauma patients, assist patients in optimal preparation for surgery as well as the delivery of acute care and recovery, improve quality of life and increase efficiency throughout the patient journey. As such, patients and clinicians are supported in the pre-operative, intra-operative and post-operative phases. By improving treatment selection, educating patients, managing patient expectations and achieving more predictable postoperative outcomes, the SFC project within Secur-e-Health aims to reduce the overall cost of care pathway and improve the quality of life for all patients with long bone fractures.


  • Use of a telemedical research system for real-time processing of distributed on-body sensor systems with connected HIS for patient management, taking into account current and future security technology (e.g., blockchain)
  • Exploration of algorithms (AI/ML technology) for preventive detection of fracture-prone movements after trauma to the long tubular bones in conjunction with minimal sensor setup of an orthosis while maintaining high reliability
  • AI analysis method for motion pattern recognition
  • Implementation of a real-time alarm system for an orthosis with the aim of alerting before the movement is performed (intention recognition)
  • Embedding of the obtained analysis results into a sensor-based Clinical Pathway

External Leader

Marc Guilbert (Kelvin Zero, Kanada)
Concept for General Improvements in the Treatment of Femoral Shaft Fractures with an Intramedullary Nail:

Siegel, Finn and Buj, Christian and Schwanbeck, Ralf and Petersik, Andreas and Hoffmann, Ulrich and Kemper, Jakob and Hildebrand, Frank and Kobbe, Philipp and Eschweiler, Jörg and Greven, Johannes and Merfort, Ricarda and Freimann, Christian and Schwaiger, Astrid and Aschwege, Frerk; Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies; 2023

Evaluating the Viability of Neural Networks for Analysing Electromyography Data in Home Rehabilitation: Estimating Foot Progression Angle:

Siegel, Finn and Buj, Christian and Merfort, Ricarda and Hein, Andreas and Aschwege, Frerk; Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies; 2024

Kelvin Zero
Perceiv Research Inc.
Institute of Microelectronic Applications
CSIT Finland Oy
VTT Technical Research Centre of Finland Ltd.
Oncare GmbH
Stryker Trauma GmbH
University Hospital RWTH Aachen
Academic Medical Center of the University of Amsterdam (AMC)
Almende BV
Eindhoven University of Technology
KnowL Solutions B.V.
PS-Tech BV
Stichting ZorgTTP
UMC Utrecht
Hospital University Fernando Pessoa
Instituto Superior de Engenharia do Porto (ISEP)
MTG Research & Development Lab
Acd Bilgi Islem ltd.sti.
Bilbest Bilişim Sağlık Eğitim Dış Ticaret ve Sanayi Ltd.Şti.
LiveWell Giyilebilir Saglik Urun Hizmet ve Teknolojileri San. ve Tic. A.S.
Softtech Yazılım Teknolojileri


Start: 01.11.2021
End: 31.10.2024

Source of funding