NFC based dataset annotation within a behavioral alerting platform

Abstract

Pervasive and ubiquitous computing increasingly relies on data-driven models learnt from large datasets. This learning process requires annotations in conjunction with datasets to prepare training data. Ambient Assistive Living (AAL) is one application of pervasive and ubiquitous computing that focuses on providing support for individuals. A subset of AAL solutions exist which model and recognize activities/behaviors to provide assistive services. This paper introduces an annotation mechanism for an AAL platform that can recognize, and provide alerts for, generic activities/behaviors. Previous annotation approaches have several limitations that make them unsuited for use in this platform. To address these deficiencies, an annotation solution relying on environmental NFC tags and smartphones has been devised. This paper details this annotation mechanism, its incorporation into the AAL platform and presents an evaluation focused on the efficacy of annotations produced. In this evaluation, the annotation mechanism was shown to offer reliable, low effort, secure and accurate annotations that are appropriate for learning user behaviors from datasets produced by this platform. Some weaknesses of this annotation approach were identified with solutions proposed within future work.