Evidence map›Paper›PMID 32872516›Full record

ArticleSensors (Basel, Switzerland)2020

SALON: Simplified Sensing System for Activity of Daily Living in Ordinary Home.

Tomokazu Matsui, Kosei Onishi, Shinya Misaki, Manato Fujimoto, Hirohiko Suwa, Keiichi Yasumoto

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 19% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Interdisciplinary perspectives on privacy awareness in lifelogging technology development.Journal of ambient intelligence and humanized computing · 2023
    Article
  7. Article
  8. Article
  9. Technology Used to Recognize Activities of Daily Living in Community-Dwelling Older Adults.International journal of environmental research and public health · 2020
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Tomokazu MatsuiNara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.
Kosei OnishiNara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.
Shinya MisakiNara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.
Manato FujimotoNara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.ORCID 0000-0002-6171-5697
Hirohiko SuwaNara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.ORCID 0000-0002-8519-3352
Keiichi YasumotoNara Institute of Science and Technology, Ikoma, Nara 630-0192, Japan.ORCID 0000-0003-1579-3237
Nara Institute of Science and Technology · JPRIKEN Center for Advanced Intelligence Project · JP

Funding

JSPS 19H05665
6 · The paper itself

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.

Indexed as

Activities of Daily LivingAgingAgedElectronicsHousingHumansPrivacydaily activity recognitionenergy harvesting sensormachine learningsimple installation sensing system

Identifiers

PMID32872516
PMCPMC7506971
OpenAlexW3082706505

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.