Abstract
We propose automated probabilistic models of everyday activities (AM-EvA) as a novel technical means for the perception, interpretation, and analysis of everyday manipulation tasks and activities of daily life. AM-EvAs are detailed, comprehensive models describing human actions at various levels of abstraction from raw poses and trajectories to motions, actions and activities. They integrate several kinds of action models in a common, knowledge-based framework to combine observations of human activities with a-priori knowledge about actions. AM-EvAs enable robots and technical systems to analyze actions in the complete situation and activity context. They make the classification and assessment of actions and situations objective and can justify the probabilistic interpretation with respect to the activities the concepts have been learned from. AM-EvAs allow to analyze and compare the way humans perform actions which can help with autonomy assessment and diagnosis. We describe in this paper the concept and implementation of the AM-EvA system and show example results from the observation and analysis of table-setting episodes.
Get full access to this article
View all access options for this article.