Semantic Matchmaking for Kinect-Based Posture and Gesture Recognition

Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A general data model and the corresponding ontology provide the formal underpinning for automatic posture and gesture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking, exploiting non-standard inference services, allows to: (i) detect postures via on-the-fly comparison of the retrieved annotations with standard posture descriptions stored as instances of a proper Knowledge Base, (ii) compare subsequent postures in order to recognize gestures. The framework has been implemented in a prototypical tool and experimental tests have been carried out on a reference dataset. Preliminary results indicate the feasibility of the proposed approach.