Research Article
Implementation of Daily Functioning and Habits Building Reasoner Part of AAL Architecture
@INPROCEEDINGS{10.1007/978-3-319-74935-8_16, author={Krasimir Tonchev and Yuliyan Velchev and Pavlina Koleva and Agata Manolova and Georgi Balabanov and Vladimir Poulkov}, title={Implementation of Daily Functioning and Habits Building Reasoner Part of AAL Architecture}, proceedings={Pervasive Computing Paradigms for Mental Health. Selected Papers from MindCare 2016, Fabulous 2016, and IIoT 2015}, proceedings_a={MINDCARE \& IIOT \& FABULOUS}, year={2018}, month={3}, keywords={Daily activity monitoring Habits measurement Habit anomaly detection GMM K-means clustering}, doi={10.1007/978-3-319-74935-8_16} }- Krasimir Tonchev
Yuliyan Velchev
Pavlina Koleva
Agata Manolova
Georgi Balabanov
Vladimir Poulkov
Year: 2018
Implementation of Daily Functioning and Habits Building Reasoner Part of AAL Architecture
MINDCARE & IIOT & FABULOUS
Springer
DOI: 10.1007/978-3-319-74935-8_16
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 modification in the health status of the user.
