Evidence map›Paper›PMID 29186786›Full record

ArticleBiosensors2017

A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications.

Giovanni Diraco, Alessandro Leone, Pietro Siciliano

Abstract read
In one paragraph

Article in Biosensors, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
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

35 citing papers in PubMed.

  1. Review
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  9. A Review on Recent Advancements of Biomedical Radar for Clinical Applications.IEEE open journal of engineering in medicine and biology · 2024
    Article
  10. Heart Rate Variability Code: Does It Exist and Can We Hack It?Bioengineering (Basel, Switzerland) · 2023
    Review
  11. Article
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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

3 authors.

Giovanni DiracoNational Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Alessandro LeoneNational Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy
Pietro SicilianoNational Research Council of Italy, Institute for Microelectronics and Microsystems, 73100 Lecce, Italy

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, and privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.

Indexed as

Activities of Daily LivingRadarAgedAged, 80 and overAge FactorsAlgorithmsHumansModels, TheoreticalMonitoring, PhysiologicQuality of LifeReproducibility of ResultsROC CurveTelemetryVital Signsfall detectionheart ratemicro-Dopplerrespiration ratesupervisedultra-wideband radarunsupervisedvital signs monitoring

Identifiers

PMID29186786
PMCPMC5746778

What OpenQuestion holds

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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.