|Autors: Tonchev, K., Velchev, Y. S., Koleva, P. H., Manolova, A. H., Balabanov, G. R., Poulkov, V. K.|
Title: Implementation of daily functioning and habits building reasoner part of AAL architecture
Keywords: Daily activity monitoring; GMM; Habit anomaly detection; Habits measurement; K-means clustering
Abstract: Individuals with Mild Cognitive Impairment (MCI) currently have few treatment options against memory loss. Solutions for caring for the elderly both efficacious and cost-effective are given by Ambient Assisted Living (AAL) architecture, promising the improvement of the Quality of Life (QoL) of patients. QoL factors that are important for the MCI patients include mood, pleasant engagements, physical mobility and health, and the ability to perform activities of daily living. In this paper, we propose a daily activity reasoner that monitors, measures and analyses in real time several everyday events for building habits diary and detecting abnormal behavior of the user, part of an effective AAL system. The proposed solution is based on a combination of mean shift clustering algorithm. The reasoner offers two primary functionalities: habits building and duration and frequency of events. The reasoner can predict the behavior and detect (slow or fast) changes that might indicate ..
Copyright ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus