School of Engineering

Human-Centered & Medical Computing

We develop human-centered AI for the real world.

We address challenges in healthcare, industry, and society by combining artificial intelligence, data science, and domain expertise. Working closely with healthcare providers, industry, public organizations, and academic partners, we develop solutions that are scientifically rigorous, practically relevant, and designed for deployment in real-world environments.

Our work spans Digital Healthcare & Medical AI, Human-Centered AI for Society, and Adaptive Interaction Systems. Through interdisciplinary research and innovation, we translate advances in artificial intelligence into technologies that support decision-making, improve processes, and create measurable impact.

We actively collaborate with healthcare providers, industry partners, public organizations, and international research institutions through joint research projects, technology transfer activities, and publicly funded innovation programs.

Human-Centered & Medical Computing at a Glance

The Human-Centered & Medical Computing (HCC) group develops human-centred AI and data-driven technologies for healthcare, industry, and society. Our research spans medical AI, computer vision, multimodal learning, and adaptive interaction systems, combining methodological innovation with real-world applications and interdisciplinary collaboration.

Research Areas:

  • Digital Healthcare & Medical AI
  • Human-Centered AI for Society
  • Adaptive Interaction Systems

Core Expertise:
Artificial Intelligence · Computer Vision · Multimodal AI · Foundation Models · Data Science · Human-AI Interaction

Collaboration:
Healthcare Providers · Industry Partners · Public Organizations · International Research Institutions

Research Areas

Digital Healthcare & Medical AI

Healthcare decisions increasingly depend on the integration of medical images, physiological signals, clinical reports, and electronic health records. Our research develops methods that combine these heterogeneous data sources to support diagnosis, treatment planning, risk assessment, and patient management.

We work on medical imaging, multimodal learning, foundation models, clinical decision support systems, and digital biomarkers. Particular emphasis is placed on developing robust and clinically relevant methods that can be evaluated and deployed in healthcare settings.

Current application areas include radiology, nuclear medicine, cardiology, and digital health.

Computer Vision & Multimodal AI for Society

Many societal and environmental challenges are characterized by large volumes of visual, textual, sensor, and geospatial data. Our research develops methods that transform these heterogeneous data sources into actionable knowledge for decision-making and planning.

We work on computer vision, multimodal learning, foundation models, geospatial analytics, and predictive modeling. Application areas include sustainable agriculture, environmental monitoring, public health, industrial inspection, and digital inclusion.

Projects are developed in close collaboration with domain experts and stakeholders to ensure scientific rigor, practical relevance, and long-term impact.

Adaptive Interaction Systems

Effective digital technologies must not only generate insights but also support meaningful interaction with users. Our research focuses on the design, development, and evaluation of adaptive systems that combine multimodal sensing, intelligent data analysis, and user-centered interfaces.

We develop mobile, web-based, and selected extended reality (XR) applications that integrate information from physiological signals, behavioral patterns, user interactions, voice, movement, and contextual data. The goal is to create systems that provide reliable assessments and personalized support while remaining practical for deployment in real-world environments.

Current application areas include stress and resilience assessment, breathing and biofeedback interventions, cognitive assessment and training, digital biomarkers, and inclusive approaches for capturing patient-reported outcomes and symptoms.

Former Research Areas

Mobile Systems

Over the years, the group has developed extensive expertise in mobile computing, and speech technologies. This work explored how visual, auditory, and sensor-based information can be combined to support intuitive interaction between people and digital systems.

Research activities included mobile and wearable applications and multimodal user interfaces. Particular emphasis was placed on user-centered design, scene understanding, and the integration of emerging interaction technologies into practical applications.

ICT-Accessibility

ICT accessibility focuses on the research and development of ICT-based solutions to reduce barriers for people with disabilities and older people, whether through barrier-free user interfaces, barrier-free access to digital information or barrier-free mobility. In doing so, we are increasingly using AI-based approaches.