CAVIAR: Context-driven Active and Incremental Activity Recognition

Bettini, Cláudio; Civitarese, Gabriele; Presotto, Riccardo

doi:10.1016/j.knosys.2020.105816

Cited by 34 publications

(42 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The information about the context that surrounds the user (e.g., semantic location, weather condition, time of the day, etc.) can be used to significantly expand the set of considered activities and to better discriminate activities with similar motion patterns that are generally executed in different context conditions (e.g., sitting and sitting on a bus) [8].…”

Section: Related Workmentioning

confidence: 99%

“…Closer to our approach are previous works on hybrid methods, in which the machine learning prediction is refined by knowledge-based reasoning over context data [5], [8]. Indeed, semantic reasoning can exclude from the prediction those activities that are highly unlikely according to the current context (e.g., if the user is at the park, it is highly unlikely that she is brushing teeth).…”

Section: Related Workmentioning

confidence: 99%

“…Upon receiving usable feedback, the module sends a new labeled sample to the incremental statistical model. More details about the Incremental Statistical Model and the Prediction Confidence Evaluation module can be found in [8]. In the following, we focus on the Ontological Probabilistic Model.…”

Section: Overall Architecture Of Procaviarmentioning

confidence: 99%

“…[7]. Following this promising research direction, we recently proposed CAVIAR [8], a hybrid semisupervised and knowledge-based activity recognition framework. The main drawback of those hybrid approaches is that they are based on knowledge-based models that use rigid rules to model only the most common scenarios to exclude from the final prediction activities performed in unlikely (but still possible) context conditions.…”

Section: Introductionmentioning

confidence: 99%

“…ProCAVIAR also uses active learning to continuously improve the semi-supervised machine learning model. Differently from CAVIAR [8], that uses rigid ontological reasoning for context-refinement, ProCAVIAR proposes an original application of probabilistic ontologies, obtaining a more realistic model of common knowledge by capturing the probabilistic relationships between activities and the context in which they are performed.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

et al. 2020

Self Cite

View full text Add to dashboard Cite

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user's movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semisupervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Overall Architecture Of Procaviarmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations