Stream Reasoning approach for Anticipating Human Activities in Ambient Intelligence environments

The advent of the internet of things (IoT) and artificial intelligence (AI) technologies enable continuous data generation, also known as data streams. Considering data streams is required for ambient intelligence (AmI) systems for context-aware assistance services. This paper proposes a hybrid approach combining deep learning models and probabilistic commonsense reasoning over data streams to anticipate human activities. The reasoning is performed using the event calculus formulated in answer set programming (ECASP); the latter allows for abductive and temporal reasoning, which enables an eXplainable AI (XAI) approach. An activity context ontology is exploited for the reasoning axiomatisation. Several experiments were conducted to demonstrate the effectiveness of the proposed approach in terms of accuracy and computation time.