Project Details
Projekt
Lifespan AI - Project M2: Lifespan Knowledge Representation
Applicants
Professor Dr. Michael Beetz, Ph.D.; Dr. Felix Putze
Subject Area
Epidemiology and Medical Biometry/Statistics
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 459360854
This project investigates computational methods for representation, reasoning, and modelling of capabilities and impairments, causally related to diseases, of individuals performing everyday activities. The goal is to make predictions about whether individuals need support and for what, how their impairments progress over time, and how this depends on contextual factors, like the structuring of the environment or fatigue. To this end, our project will design, realise, and examine the computational principles of a Lifespan Predictive Modelling Engine (L-PME), a hybrid knowledge representation and reasoning framework that accompanies individuals, records their everyday activities, and maintains individual models of impairments in a lifelong learning process. To represent an individual person, L-PME customises a cognitive digital twin from a generic impairment-aware knowledge representation of everyday activities that captures (1) the development of disease-induced impairments in context of a person's changing situations, needs, and tasks, (2) the causal influence of capabilities and impairments on everyday manipulation tasks, and (3) the latent interdependence of behaviours in different everyday activities to answer open queries and predict performance and behaviour. We will investigate the foundations of the representation, reasoning, and modelling methods by applying it to persons wearing physical-virtual age suits during everyday activities, such as setting the table. This allows us to investigate the principles of L-PME under observable and controllable conditions by changing the parameters of the physical-virtual age suits systematically in an expert-in-the-loop process.
DFG Programme
Research Units
