Evidence map›Paper›PMID 39275445›Full record

ArticleSensors (Basel, Switzerland)2024

Analysis of Gait Kinematics in Smart Walker-Assisted Locomotion in Immersive Virtual Reality Scenario.

Matheus Loureiro, Arlindo Elias, Fabiana Machado, Marcio Bezerra, Carla Zimerer, Ricardo Mello, Anselmo Frizera

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. 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

7 authors.

Matheus LoureiroGraduate Program in Electrical Engineering, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.
Arlindo EliasGraduate Program in Physiotherapy, Estacio de Sa University, Vitória 29092-095, ES, Brazil.ORCID 0009-0007-9169-080X
Fabiana MachadoGraduate Program in Informatics, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.
Marcio BezerraGraduate Program in Electrical Engineering, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.
Carla ZimererGraduate Program in Electrical Engineering, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.
Ricardo MelloGraduate Program in Electrical Engineering, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.ORCID 0000-0003-0420-4273
Anselmo FrizeraGraduate Program in Electrical Engineering, Federal University of Espírito Santo, Vitória 29075-910, ES, Brazil.ORCID 0000-0002-0687-3967

Funding

Brazilian National Council for Scientific and Technological Development - CNPq 308155/2023-8Brazilian National Council for Scientific and Technological Development - CNPq 403753/2021-0Financiadora de Estudos e Projetos - FINEP 2784/20Fundação de Amparo à Pesquisa e Inovação do Espírito Santo - FAPES 2022-C5K3HFundação de Amparo à Pesquisa e Inovação do Espírito Santo - FAPES 2022-D48XBFundação de Amparo à Pesquisa e Inovação do Espírito Santo - FAPES 2023-XDHHW
6 · The paper itself

Abstract

The decline in neuromusculoskeletal capabilities of older adults can affect motor control, independence, and locomotion. Because the elderly population is increasing worldwide, assisting independent mobility and improving rehabilitation therapies has become a priority. The combination of rehabilitation robotic devices and virtual reality (VR) tools can be used in gait training to improve clinical outcomes, motivation, and treatment adherence. Nevertheless, VR tools may be associated with cybersickness and changes in gait kinematics. This paper analyzes the gait parameters of fourteen elderly participants across three experimental tasks: free walking (FW), smart walker-assisted gait (AW), and smart walker-assisted gait combined with VR assistance (VRAW). The kinematic parameters of both lower limbs were captured by a 3D wearable motion capture system. This research aims at assessing the kinematic adaptations when using a smart walker and how the integration between this robotic device and the VR tool can influence such adaptations. Additionally, cybersickness symptoms were investigated using a questionnaire for virtual rehabilitation systems after the VRAW task. The experimental data indicate significant differences between FW and both AW and VRAW. Specifically, there was an overall reduction in sagittal motion of 16%, 25%, and 38% in the hip, knee, and ankle, respectively, for both AW and VRAW compared to FW. However, no significant differences between the AW and VRAW kinematic parameters and no adverse symptoms related to VR were identified. These results indicate that VR technology can be used in walker-assisted gait rehabilitation without compromising kinematic performance and presenting potential benefits related to motivation and treatment adherence.

Indexed as

GaitVirtual RealityAgedBiomechanical PhenomenaExoskeleton DeviceFemaleHumansLocomotionMaleRoboticsWalkersWalkinggait analysismotion capturerehabilitation roboticssmart walkervirtual reality

Identifiers

PMID39275445
PMCPMC11398063

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.