Enhancing ontological reasoning with uncertainty handling for activity recognition

Handling uncertainty is a challenge in activity recognition. Uncertainty can be due to sensor errors (e.g. run out of batteries, imprecise outputs, missing activations etc.) and communication failures. and variability in human activities. These issues may significantly influence the accuracy of activity recognition. Data-driven approaches use machine learning techniques such as Decision Tree, na├»ve Bayes…

Reasoning under uncertainty

Real-world is inherently uncertain. Uncertainty indicates the lack of confidence in an event or decision. It arises from different sources and in various forms. In the context of activity recognition, uncertainty may be due to sensor errors, communication failures and variability in human activities. Reasoning under uncertainty is a process of deducing new knowledge based…

Comparison between evidence (Dempster-Shafer) theory and Bayesian theory

Both Bayesian theory and Dempster-Shafer (evidence) theory assign non-negative weights to set of events. In Bayesian theory, the finite set of possible events denoted by , each individual event, is assigned a non-negative weight called probability denoted by . The probabilities satisfy the following properties. for all In Dempster-Shafer theory, the finite set called frame…

Uncertainty in Ontology Approach

Ontology is the most widely used tool to integrate semantic into activity recognition. However, besides lack of temporal reasoning, ontology cannot deal with uncertainty [1]. Diaz et al. proposed a fuzzy ontology to solve this limitation. But, fuzzy approach allows more precise knowledge representation rather than deciding among multiple hypotheses and having a more coherent…