Evidence map›Paper›PMID 39184960›Full record

ArticleIEEE open journal of engineering in medicine and biology2024

Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment.

Sevgi Z Gurbuz, Mohammad Mahbubur Rahman, Zahra Bassiri, Dario Martelli

Abstract read
In one paragraph

Article in IEEE open journal of engineering in medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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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

3 citing papers in PubMed.

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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.

Sevgi Z GurbuzDepartment of Electrical and Computer EngineeringUniversity of Alabama Tuscaloosa AL 35487 USA.ORCID https://orcid.org/0000-0001-7487-9087
Mohammad Mahbubur RahmanAdvanced Radar Systems Team of Aptiv Corporation Kokomo IN 13085 USA.ORCID https://orcid.org/0000-0002-4883-9767
Zahra BassiriCenter for Motion Analysis in the Division of Orthopedic Surgery at Connecticut Children's Farmington CT 06032 USA.ORCID https://orcid.org/0000-0002-2331-3099
Dario MartelliDepartment of Orthopedics and Sports MedicineMedStar Health Research Institute Baltimore MD 21218 USA.ORCID https://orcid.org/0000-0002-1903-5372

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

Indexed as

Fall risk assessmentgait parameter estimationmicro-dopplerradarskeleton estimation

Identifiers

PMID39184960
PMCPMC11342925

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.