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Biomedical Data acquistion- Autonomic nervous system
Univerzita Komenskeho V Bratislave (UK BA)
The laboratory performs research and scientific activities in the research of autonomic nervous system regulation. It uses Finometer (FMS) and Nexfin (Edwards Lifesciences) devices, which are used for non-invasive beat-to-beat blood pressure monitoring using volume-clamp method, and MedGem (Microlife) for determination of basal metabolism. It uses ECG Cardiofax 9620M (Nihon Cohden) for monitoring the electrical activity of the heart, RespiTrace (NIMS) for non-invasive respiratory monitoring by impedance method using chest and abdominal belts, CardioScreen 2000 (Medis) for non-invasive monitoring of various parameters associated with pumping function of the heart (systolic volume, systolic time intervals, etc.). It uses VaSera VS-1500N (Fukuda Denshi) characterization of arterial stiffness, EndoPAT 2000 (Itamar Medical) - characterization of endothelial dysfunction, InBody J10 (Biospace) - analysis of body composition, ECG (BTL; Pola V8) - non-invasive continuous bio signals monitoring, Finometer (Finapres medical systems), blood pressure, Flex Comp Infinity, Thought Technology for measuring the electrodermal activity, respiration, peripheral temperature, Neuroptics PLR200, pupilometry, Eyetracking (Pupil Labs) for eye movements, InBody 120 - body composition analysis.
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BioSignal Suite: Design and Validation of Biomedical Signal Processing Pipelines
Karolinska Institutet (KI)
## Overview
This service supports the development and validation of biomedical signal processing pipelines for healthcare applications. SMAILE at Karolinska Institutet provides guidance for processing a range of biosignals, including ECG, EEG, EMG, respiration, accelerometers, gyroscope, and electrical bioimpedance. The focus is on signal filtering, feature extraction, event detection, and pipeline validation, aligned with clinical research standards.
### We provide expertise in:
• Biomedical signal filtering and preprocessing
• Feature extraction and clinical event annotation
• Signal Analysis and Modeling
• Quality assurance and reproducibility
### How can the service help you?
Many early-stage companies and research projects lack robust, validated signal pipelines. This service enables reproducible analysis, clinical readiness, and regulatory alignment for biosignal-driven products and studies.
### How will the service be delivered?
Can be delivered virtually or in conjunction with lab access. Clients share sample datasets, objectives, and processing goals. Duration: 2–6 weeks based on complexity.
## Additional Information
### Provider Description
SMAILE provides advanced support in biomedical signal analytics, with experience in wearable health monitoring, digital phenotyping, and clinical-grade biosignal systems.
### Technical Description
Pipelines are constructed and validated using Python (SciPy, MNE, NeuroKit), MATLAB, or other signal tools.
### Service Customization
Clients may request focused support for real-time processing, offline batch analysis, or integration with AI modules. Output formats, sampling rates, and noise tolerance are adjustable.
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Clinical Feasibility Assessment & Real-World Validation
Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev (Fraunhofer)
**Who Can Benefit:**
- Clinical Researchers and Investigators: For gaining insights into the feasibility of proposed studies and enhancing study designs with practical clinical input.
- Healthcare Institutions: To expand research capabilities by connecting with technical and clinical experts and facilities.
- Research and Development Teams: For testing new technologies or methodologies in applicable clinical contexts, ensuring innovative solutions are viable and effective.
**Key Features:**
- Technical Feasibility Assessment: Organizes and conducts comprehensive technical feasibility studies to evaluate the practicality and potential of clinical research projects.
- Connection to Clinical Personnel: Facilitates collaboration between researchers and clinical experts at University Clinic Erlangen, ensuring access to expertise and resources necessary for study success.
- Access to Real-Life and Laboratory Test Environments: Provides opportunities to conduct studies within associated clinics, leveraging previous experience in cardiology, neurology, and biomechanics to ensure practical and applicable results.
**Possible Applications:**
- Project Feasibility Evaluation: Assists in determining the feasibility of clinical research projects, helping to identify potential challenges and solutions early in the development phase.
- Research Collaboration: Enhances research outcomes through the integration of clinical expertise and real-world testing environments, ensuring studies are rooted in clinical reality.
**Who We Are:**
The **Fraunhofer Insitute for Integrated Circuits (Fraunhofer IIS)** has established the **"Center for Sensor Technology and Digital Medicine" (CEMDIS)** in cooperation with the Universitätsklinikum Erlangen and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) to enhance modern healthcare through **innovative sensor technology** and **digital solutions**. This center focuses on integrating **innovative medical technologies** such as **wearables** and **robotic systems** to support **medical diagnostics**, **patient monitoring** and **evaluating patient-specific therapies** by providing digital health solutions für real-life healthcare. Located at the Universitätsklinikum Erlangen, it offers unique infrastructures for the **development**, **integration**, and **validation** of novel health technologies, providing companies opportunities for **technological advancements**. For more information, visit the [Fraunhofer IIS website](https://www.iis.fraunhofer.de/de/ff/sse/health/zentrum-sensorik-medizin.html).
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Generation of synthetic virtual cohorts leveraging statistical and data-driven methods and featuring realistic anatomies, flow fields, and boundary conditions.
Politecnico Di Milano (POLIMI)
Generation of synthetic virtual cohorts leveraging statistical and data-driven methods and featuring realistic anatomies, flow fields, and boundary conditions. These virtual populations can serve multiple purposes, from in silico trials for testing new devices and procedures, to training large deep learning models for disease-specific tasks and discoveries.
Method Description:
The service provides synthetic datasets for AI algorithm development, AI algorithm testing and digital twin based simulations (fluidynamic/structural simulations, fluid structure interaction)
Method reference:
https://doi.org/10.1016/j.cmpb.2023.107468
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Patient-to-Pipeline: Regulatory-Compliant Clinical Data Collection
Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev (Fraunhofer)
**Who Can Benefit:**
- MedTech & AI Companies: For obtaining high-quality, ethically sourced clinical datasets to fuel algorithm development without navigating hospital bureaucracy.
- Clinical Researchers: For systematic, regulation-compliant data collection with full documentation and audit trail.
**Key Features:**
- End-to-end management of ethical approval processes (ethics committee submission, informed consent design)
- Prospective data collection within Universitätsklinikum Erlangen clinical departments
- Expert clinical annotation and ground truth labeling by domain specialists
- Full GDPR compliance with pseudonymization/anonymization pipelines
- Structured data output compatible with downstream AI training and analysis workflows
**Possible Applications:**
- Wearable Sensor Studies (e.g. Cardiology): Prospective collection of continuous ECG, PPG, or blood pressure data from cardiac patients for digital biomarker development and remote monitoring algorithm training.
- Motion & Gait Analysis (Biomechanics): Systematic acquisition of IMU, force plate, and motion capture data in clinical and sports lab settings for movement disorder assessment, rehabilitation tracking, or athletic performance evaluation.
- Neurological Signal Acquisition: Collection of EEG, EMG, or tremor sensor data from neurological patient cohorts to support AI-based diagnostic or therapy monitoring tools.
- Real-World Evidence Datasets: Longitudinal data collection from ambulatory patients in everyday settings, enabling the development of algorithms that generalize beyond controlled lab environments.
- Athletic Performance & Injury Prevention: Structured data gathering during sports science testing protocols to build datasets for injury risk prediction, load monitoring, and return-to-play decision support.
- Multi-Modal Clinical Datasets: Combined collection of sensor data, imaging, lab values, and patient-reported outcomes to enable holistic, multi-modal AI model development.
**Who We Are:**
The **Fraunhofer Insitute for Integrated Circuits (Fraunhofer IIS)** has established the **"Center for Sensor Technology and Digital Medicine" (CEMDIS)** in cooperation with the Universitätsklinikum Erlangen and the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) to enhance modern healthcare through **innovative sensor technology** and **digital solutions**. This center focuses on integrating **innovative medical technologies** such as **wearables** and **robotic systems** to support **medical diagnostics**, **patient monitoring** and **evaluating patient-specific therapies** by providing digital health solutions für real-life healthcare. Located at the Universitätsklinikum Erlangen, it offers unique infrastructures for the **development**, **integration**, and **validation** of novel health technologies, providing companies opportunities for **technological advancements**. For more information, visit the [Fraunhofer IIS website](https://www.iis.fraunhofer.de/de/ff/sse/health/zentrum-sensorik-medizin.html).
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Pulse Wave Cartography Laboratory
Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU)
Services:
- Synchronized measurement and recording of radar and reference signals in the body
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