Reasoning with smart objects’ affordance for personalized behavior monitoring in pervasive information systems

Human Activity Recognition Models in Ontology Networks

“…Other hybrid logic-statistical frameworks have been proposed in the literature. For instance, in [20]- [22] activities are described with a combination of probabilistic models and semantic constraints. Other contextualisation techniques exploit a hybrid solution via the augmentation of ontologies with specific ML-based algorithms, which reason in terms of activity patterns [23], [24], or infer concepts through a learning approach [25].…”

“…Several ontology-based representations have been evaluated in [25] on the basis of the context they define, e.g., sensor events, sensor hierarchies, human postures or locations. In [22], the context is defined in terms of affordances, and it is based on relevant knowledge about space, time and human attitudes. In these approaches, the need for efficient reasoning on data typically leads to ontologies that are engineered either for computational efficiency or expressivity, i.e., they precisely describe a human activity in detail.…”

HealthXAI: Collaborative and explainable AI for supporting early diagnosis of cognitive decline

“…Notably, some existing methods also rely on personalization features addressed to people with disabilities [49]. Hence, in this paper, we assume the existence of an effective module for action/activity segmentation and recognition, but we do not make any assumption about the actual implementation of that module.…”

Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers

“…Activity recognition approaches based on the domain Knowledge uses ontological modeling and semantic reasoning (Chen et al 2012). An ontology based reasoning framework is discussed by Matassa and Riboni (2019), which recognizes the normal and anomalous behavior of the elderly. A generic hybrid approach to recognize the composite activities occurring in a sequential or parallel order, such as preparing dinner or dish washing, integrates ontology, temporal knowledge and the inference rules (Okeyo et al 2014).…”