Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network.

Sora Hayashi, Kenshi Saho, Keitaro Shioiri, Masahiro Fujimoto, Masao Masugi

Open access · goldAbstract read

In one paragraph

Article 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 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.4field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 8 citations in OpenAlex.

Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults.Sensors (Basel, Switzerland) · 2022
Article

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

5 authors at 3 institutions in 1 country.

Sora HayashiGraduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.

Kenshi SahoGraduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.ORCID 0000-0003-2088-1231

Keitaro ShioiriGraduate School of Engineering, Toyama Prefectural University, Toyama 939-0398, Japan.

Masahiro FujimotoHuman Augmentation Research Center, National Institute of Advanced Industrial Science and Technology, Chiba 277-0882, Japan.

Masao MasugiGraduate School of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.

Ritsumeikan University · JPNational Institute of Advanced Industrial Science and Technology · JPToyama Prefectural University · JP

Funding

Japan Society for the Promotion of Science 18K18463

6 · The paper itself

Abstract

To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method.

Indexed as

RadarAdultAgedGaitHumansNeural Networks, ComputerWalkingDoppler radargait classificationLSTMmachine learning

Identifiers

PMID34073806

DOI10.3390/s21113643

PMCPMC8197185

OpenAlexW3164342146

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.