ArticleSensors (Basel, Switzerland)2020
SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Smart Home Technologies for Ageing in Place: A Systematic Review of Health Outcomes, Independence, Healthcare Utilisation and Implementation Considerations.Australasian journal on ageing · 2026Pooled it
- AI-Driven Activity of Daily Living Monitoring: A Comprehensive Review of Ambient, Wearable, and Fusion-Based Sensing Technologies.Annals of biomedical engineering · 2026Review
- Evaluation of Activities of Daily Living: Current Insights and Future Horizons.Annals of geriatric medicine and research · 2025Review
- Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags.Sensors (Basel, Switzerland) · 2024Article
- Stress Estimation Using Biometric and Activity Indicators to Improve QoL of the Elderly.Sensors (Basel, Switzerland) · 2023Article
- Interdisciplinary perspectives on privacy awareness in lifelogging technology development.Journal of ambient intelligence and humanized computing · 2023Article
- Development of an intrinsic health risk prediction model for camera-based monitoring of older adults living alone.Scientific reports · 2022Article
- Mathematical Criteria for a Priori Performance Estimation of Activities of Daily Living Recognition.Sensors (Basel, Switzerland) · 2022Article
- Technology Used to Recognize Activities of Daily Living in Community-Dwelling Older Adults.International journal of environmental research and public health · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
As aging populations continue to grow, primarily in developed countries, there are increasing demands for the system that monitors the activities of elderly people while continuing to allow them to pursue their individual, healthy, and independent lifestyles. Therefore, it is required to develop the activity of daily living (ADL) sensing systems that are based on high-performance sensors and information technologies. However, most of the systems that have been proposed to date have only been investigated and/or evaluated in experimental environments. When considering the spread of such systems to typical homes inhabited by elderly people, it is clear that such sensing systems will need to meet the following five requirements: (1) be inexpensive; (2) provide robustness; (3) protect privacy; (4) be maintenance-free; and, (5) work with a simple user interface. In this paper, we propose a novel senior-friendly ADL sensing system that can fulfill these requirements. More specifically, we achieve an easy collection of ADL data from elderly people while using a proposed system that consists of a small number of inexpensive energy harvesting sensors and simple annotation buttons, without the need for privacy-invasive cameras or microphones. In order to evaluate the practicality of our proposed system, we installed it in ten typical homes with elderly residents and collected the ADL data over a two-month period. We then visualized the collected data and performed activity recognition using a long short-term memory (LSTM) model. From the collected results, we confirmed that our proposed system, which is inexpensive and non-invasive, can correctly collect resident ADL data and could recognize activities from the collected data with a high recall rate of 72.3% on average. This result shows a high potential of our proposed system for application to services for elderly people.
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What OpenQuestion holds
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.