Evidence map›Paper›PMID 39599118›Full record

ArticleSensors (Basel, Switzerland)2024

Prediction of Margin of Gait Stability by Using Six-DoF Motion of Pelvis.

Tomohito Kuroda, Shogo Okamoto, Yasuhiro Akiyama

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Tomohito KurodaDepartment of Computer Science, Tokyo Metropolitan University, 6-6, Asahigaoka, Hino 191-0065, Japan.ORCID 0009-0006-8830-8430
Shogo OkamotoDepartment of Computer Science, Tokyo Metropolitan University, 6-6, Asahigaoka, Hino 191-0065, Japan.ORCID 0000-0003-2116-7734
Yasuhiro AkiyamaFaculty of Textile Science and Technology, Shinshu University, 3-15-1 Tokida, Ueda 386-8567, Japan.ORCID 0000-0002-4169-3734

Funding

Ministry of Education, Culture, Sports, Science and Technology KAKENHI #23K21021
6 · The paper itself

Abstract

Unstable gait increases the risk of falls, posing a significant danger, particularly for frail older adults. The margin of stability (MoS) is a quantitative index that reflects the risk of falling due to postural imbalance in both the anterior-posterior and mediolateral directions during walking. Although MoS is a reliable indicator, its computation typically requires specialized equipment, such as motion capture systems, limiting its application to laboratory settings. To address this limitation, we propose a method for estimating MoS using time-series data from the translational and angular velocities of a single body segment-the pelvis. By applying principal motion analysis to process the multivariate time-series data, we successfully estimated MoS. Our results demonstrate that the estimated MoS in the mediolateral direction achieved an RMSE of 0.88 cm and a correlation coefficient of 0.72 with measured values, while in the anterior-posterior direction, the RMSE was 0.73 cm with a correlation coefficient of 0.87. These values for the mediolateral direction are better than those obtained in previous studies using only the three translational velocity components of the pelvis, whereas the values for the anterior direction are comparable to previous approaches. Our findings suggest that MoS can be reliably estimated using six-axial kinematic data of the pelvis, offering a more accessible method for assessing gait stability.

Indexed as

GaitPelvisPostural BalanceAccidental FallsAdultBiomechanical PhenomenaFemaleHumansMaleWalkinggait stabilityinertial measurement unitmotion capturepostural stabilityprincipal motion analysistime-series data

Identifiers

PMID39599118
PMCPMC11598456

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Registered trials

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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.