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Physiological Data and AI

Building signal-processing, multimodal data-fusion and AI methods for physiological data interpretation.

Physiological data and AI visual showing multimodal signals, analytics and data-fusion dashboards

Overview

This pillar develops data-analysis methods for extracting meaning from complex physiological signals. The work connects signal processing, multimodal data fusion, machine learning and explainable analytics to support health-monitoring research across MESH Lab application areas.

Focus areas

  • Multimodal physiological data integration and signal harmonisation
  • Digital biomarkers and interpretable feature engineering
  • AI and machine-learning methods for physiological signal interpretation
  • Validation workflows for data-driven health-monitoring research
Physiological data and AI visual showing multimodal signals, analytics and data-fusion dashboards

Methods & technologies

  • Signal processing, feature extraction and quality assessment
  • Machine learning, deep learning and model validation
  • Data fusion across wearable and physiological sensing modalities
  • Explainable and translational analytics workflows
Signal processingData fusionMachine learningDigital biomarkersExplainable AIModel validation

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People involved

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