Engineering integration of ML/AI technologies in sports and medical technology (e.g. calculation of biomechanical parameters for objective diagnostics)
Holistic intelligent medical technology through the composition of ML/AI algorithms and embedded/ubiquitous computing systems (e.g. ambulatory gait analysis for Parkinson’s patients, fall detection)
Medical ML/AI applications from the perspective of human-computer interaction research (e.g. acceptance and usability of ML/AI assistance systems in everyday clinical practice)
Basic Research
Seamless coupling of ML/AI algorithms (e.g. feature selection and classification)
Resource-efficient classification (e.g. SVMs)
Balancing between model quality and resource requirements
Unbiased evaluation of ML/AI models for multi-class problems
AI in Medicine
Basic Research
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