| Title: | "Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors" |
| Author(s): | Susnea I; Dumitriu L; Talmaciu M; Pecheanu E; Munteanu D; |
| Address: | "Department of Computer and Information Technology, University 'Dunarea de Jos' of Galati, 800146 Galati, Romania. Ioan.Susnea@ugal.ro. Department of Computer and Information Technology, University 'Dunarea de Jos' of Galati, 800146 Galati, Romania. Luminita.Dumitriu@ugal.ro. Department of Mathematics and Informatics, University 'Vasile Alecsandri' of Bacau, 600115 Bacau, Romania. mtalmaciu@ub.ro. Department of Computer and Information Technology, University 'Dunarea de Jos' of Galati, 800146 Galati, Romania. Emilia.Pecheanu@ugal.ro. Department of Computer and Information Technology, University 'Dunarea de Jos' of Galati, 800146 Galati, Romania. Dan.Munteanu@ugal.ro" |
| ISSN/ISBN: | 1424-8220 (Electronic) 1424-8220 (Linking) |
| Abstract: | "Most expert projections indicate that in 2030, there will be over one billion people aged 60 or over. The vast majority of them prefer to spend their last years at home, and almost a third of them live alone. This creates a growing need for technology-based solutions capable of helping older people to live independently in their places. Despite the wealth of solutions proposed for this general problem, there are very few support systems that can be reproduced on a larger scale. In this study, we propose a method to monitor the activity of the elderly living alone and detect deviations from the previous activity patterns based on the idea that the residential living environment can be modeled as a collection of behaviorally significant places located arbitrarily in a generic space. Then we use virtual pheromones-a concept defined in our previous work-to create images of the pheromone distribution maps, which describe the spatiotemporal evolution of the interactions between the user and the environment. We propose a method to detect deviations from the activity routines based on a simple statistical analysis of the resulting images. By applying this method on two public activity recognition datasets, we found that the system is capable of detecting both singular deviations and slow-deviating trends from the previous activity routine of the monitored persons" |
| Keywords: | "Activities of Daily Living Aged Aged, 80 and over Biosensing Techniques/*methods Home Care Services Humans *Independent Living Middle Aged Monitoring, Physiologic/*methods anomaly detection long-term activity monitoring modeling the living space virtual p;" |
| Notes: | "MedlineSusnea, Ioan Dumitriu, Luminita Talmaciu, Mihai Pecheanu, Emilia Munteanu, Dan eng Switzerland 2019/05/19 Sensors (Basel). 2019 May 16; 19(10):2264. doi: 10.3390/s19102264" |
