ArticleSensors (Basel, Switzerland)2019
Unobtrusive Monitoring the Daily Activity Routine of Elderly People Living Alone, with Low-Cost Binary Sensors.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population.Sensors (Basel, Switzerland) · 2022Trial
- Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors.Sensors (Basel, Switzerland) · 2025Article
- Recognizing Human Activity of Daily Living Using a Flexible Wearable for 3D Spine Pose Tracking.Sensors (Basel, Switzerland) · 2023Article
- In-Home Monitoring Technology for Aging in Place: Scoping Review.Interactive journal of medical research · 2022Article
- Identifying and Monitoring the Daily Routine of Seniors Living at Home.Sensors (Basel, Switzerland) · 2022Article
- Development of an Anomaly Alert System Triggered by Unusual Behaviors at Home.Sensors (Basel, Switzerland) · 2021Article
- Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN.Sensors (Basel, Switzerland) · 2021Article
- Unobtrusive Health Monitoring in Private Spaces: The Smart Home.Sensors (Basel, Switzerland) · 2021Review
- Smart Environments and Social Robots for Age-Friendly Integrated Care Services.International journal of environmental research and public health · 2020Article
- Discovering Human Activities from Binary Data in Smart Homes.Sensors (Basel, Switzerland) · 2020Article
- Zero-Shot Human Activity Recognition Using Non-Visual Sensors.Sensors (Basel, Switzerland) · 2020Article
- Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.