ArticleSensors (Basel, Switzerland)2022
The Problem of Monitoring Activities of Older People in Multi-Resident Scenarios: An Innovative and Non-Invasive Measurement System Based on Wearables and PIR Sensors.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technological applications to enhance independence in daily activities for older adults: a systematic review.Frontiers in public health · 2024Pooled it
- AI-Driven Activity of Daily Living Monitoring: A Comprehensive Review of Ambient, Wearable, and Fusion-Based Sensing Technologies.Annals of biomedical engineering · 2026Review
- Indoor Mobility Patterns Measured by PIR Sensors for Classifying Health-Related Quality of Life in Older Adults Using Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Supported Virtual Nursing at Home: A Systematic Review.Florence Nightingale journal of nursing · 2026Article
- Sensors Network and Wearables for People Activities and Wellbeing Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Factors that influence the implementation of AI-driven lifestyle monitoring in long-term care for older adults.The Gerontologist · 2025Article
- The Cooperation Between Nurses and a New Digital Colleague "AI-Driven Lifestyle Monitoring" in Long-Term Care for Older Adults: Viewpoint.JMIR nursing · 2024Article
- Empowering Smart Aging: Insights into the Technical Architecture of the e-VITA Virtual Coaching System for Older Adults.Sensors (Basel, Switzerland) · 2024Article
- Interaction with a Virtual Coach for Active and Healthy Ageing.Sensors (Basel, Switzerland) · 2023Article
- Statistical Study of User Perception of Smart Homes during Vital Signal Monitoring with an Energy-Saving Algorithm.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This paper presents an innovative multi-resident activity detection sensor network that uses the Bluetooth Low Energy (BLE) signal emitted by tags worn by residents and passive infrared (PIR) motion sensors deployed in the house to locate residents and monitor their activities. This measurement system solves the problem of monitoring older people and measuring their activities in multi-resident scenarios. Metrics are defined to analyze and interpret the collected data to understand daily habits and measure the activity level (AL) of older people. The accuracy of the system in detecting movements and discriminating residents is measured. As the sensor-to-person distance increases, the system decreases its ability to detect small movements, while still being able to detect large ones. The accuracy in discriminating the identity of residents can be improved by up to 96% using the Decision Tree (DT) classifier. The effectiveness of the measurement system is demonstrated in a real multi-resident scenario where two older people are monitored during their daily life. The collected data are processed, obtaining the AL and habits of the older people to assess their behavior.
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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.