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Engineering
Control and Systems Engineering

A Survey on Ontologies for Human Behavior Recognition

Profile image of Miguel Calvo-floresMiguel Calvo-floresProfile image of Natalia Díaz RodríguezNatalia Díaz Rodríguez
https://doi.org/10.1145/2523819
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Abstract

Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.

FAQs

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What are the advantages of using ontologies for human behavior recognition?add

The paper demonstrates that ontologies provide superior flexibility and expressiveness for contextualization, enabling better activity recognition and semantic management. This includes accurate inferencing capabilities derived from user actions and interactions within environments.

How do machine learning models compare to knowledge-based approaches in activity recognition?add

The research reveals that while machine learning models like Hidden Markov Models (HMMs) excel in data-driven scenarios, knowledge-based approaches using ontologies offer enhanced contextual understanding and lower model retraining requirements.

What limitations do current data-driven approaches face in human activity recognition?add

The survey identifies that data-driven methodologies often struggle with scalability and the inability to handle dynamic environments effectively, particularly when modeling unobserved or unsupervised human behaviors.

How does context awareness integrate with human activity modeling?add

The study indicates that context awareness models improve activity recognition by incorporating semantically rich frameworks, allowing systems to adaptively respond to various user behaviors and environments.

What role do hybrid approaches play in activity recognition systems?add

Hybrid systems that combine data-driven and knowledge-driven methods have shown to enhance activity recognition accuracy, reducing misclassification rates significantly, as evidenced by a 45.43% error reduction in specific applications.

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