S-SMART: A Unified Bayesian Framework for Simultaneous Semantic Mapping, Activity Recognition, and Tracking

摘要

The machine recognition of user trajectories and activities is fundamental to devise context-aware applications for support and monitoring in daily life. So far, tracking and activity recognition were mostly considered as orthogonal problems, which limits the richness of possible context inference. In this work, we introduce the novel unified computational and representational framework S-SMART that simultaneously models the environment state (semantic mapping), localizes the user within this map (tracking), and recognizes interactions with the environment (activity recognition). Thus, S-SMART identifies which activities the user executes where (e.g., turning a handle next to a window), and reflects the outcome of these actions by updating the world model (e.g., the window is now open). This in turn conditions the future possibility of executing actions at specific places (e.g., closing the window is likely to be the next action at this location). S-SMART works in a self-contained manner and iteratively builds the semantic map from wearable sensors only. This enables the seamless deployment to new environments.