Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors
| Autor: | Emilia Pecheanu, Dan Munteanu, Ioan Susnea, Mihai Talmaciu, Luminita Dumitriu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Living environment Biosensing Techniques 02 engineering and technology Space (commercial competition) lcsh:Chemical technology Machine learning computer.software_genre 01 natural sciences Biochemistry Binary sensors Article long-term activity monitoring anomaly detection modeling the living space virtual pheromones Analytical Chemistry Activities of Daily Living 0202 electrical engineering electronic engineering information engineering Humans Elderly people lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Aged Monitoring Physiologic Aged 80 and over business.industry 010401 analytical chemistry Middle Aged Home Care Services Atomic and Molecular Physics and Optics 0104 chemical sciences 020201 artificial intelligence & image processing Anomaly detection Independent Living Artificial intelligence Scale (map) business computer |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 19, Iss 10, p 2264 (2019) Sensors; Volume 19; Issue 10; Pages: 2264 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s19102264 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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