An Ontology-Based, Fully Probabilistic, Scalable Method for Human Activity Recognition

TL;DR

This paper introduces a scalable, ontology-based probabilistic method for human activity recognition that improves accuracy and flexibility by considering multiple candidate activities and leveraging relational databases for reasoning.

Contribution

It presents a novel scalable approach combining ontology, probabilistic reasoning, and relational databases for human activity recognition, addressing efficiency and scalability issues.

Findings

Improved recognition accuracy with multiple candidate consideration.

Enhanced system flexibility for unreliable sensor data.

Demonstrated feasibility through numerical evaluation and benchmarking.

Abstract

Efficiency and scalability are obstacles that have not yet received a viable response from the human activity recognition research community. This paper proposes an activity recognition method. The knowledge model is in the form of ontology, the state-of-the-art in knowledge representation and reasoning. The ontology starts with probabilistic information about subjects' low-level activities and location and then is populated with the assertion axioms learned from data or defined by the user. Unlike methods that choose only the most probable candidate from sensor readings, the proposed method keeps multiple candidates with the known degree of confidence for each one and involves them in decision making. Using this method, the system is more flexible to deal with unreliable readings from sensors, and the final recognition rate is improved. Besides, to resolve the scalability problem, a system is designed and implemented to do reasoning and storing in a relational database management system. Numerical evaluation and conceptual benchmarking prove the proposed system feasibility.