Evidence map›Paper›PMID 34696046›Full record

ReviewSensors (Basel, Switzerland)2021

State-of-the-Art Wearable Sensors and Possibilities for Radar in Fall Prevention.

José Gabriel Argañarás, Yan Tat Wong, Rezaul Begg, Nemai Chandra Karmakar

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
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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

4 authors.

José Gabriel ArgañarásElectric and Computer Systems Engineering Department, Monash University, Clayton, VIC 3800, Australia.ORCID 0000-0003-4902-1833
Yan Tat WongElectric and Computer Systems Engineering Department, Monash University, Clayton, VIC 3800, Australia.
Rezaul BeggInstitute for Health & Sport, Victoria University, Melbourne, VIC 3032, Australia.ORCID 0000-0002-3195-8591
Nemai Chandra KarmakarElectric and Computer Systems Engineering Department, Monash University, Clayton, VIC 3800, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.

Indexed as

Accidental FallsWearable Electronic DevicesAlgorithmsGaitHumansQuality of LifeRadarfall preventiongait analysisradarsensor networksmart shoeswearableswireless

Identifiers

PMID34696046
PMCPMC8539234

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.