Evidence map›Paper›PMID 41755150›Full record

ArticleSensors (Basel, Switzerland)2026

Dynamic Gait Stability Estimated Using One or Two Inertial Measurement Units Worn on the Human Body.

Haoyun Peng, Shogo Okamoto, Hiroki Watanabe, Yasuhiro Akiyama

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

4 authors.

Haoyun PengDepartment of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan.
Shogo OkamotoDepartment of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan.ORCID 0000-0003-2116-7734
Hiroki WatanabeDepartment of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan.
Yasuhiro AkiyamaDepartment of Mechanics and Robotics, Shinshu University, 3-15-1 Tokita, Ueda, Nagano 386-8567, Japan.ORCID 0000-0002-4169-3734

Funding

Okawa Foundation 24-12
6 · The paper itself

Abstract

The margin of stability (MoS) is a metric used to assess dynamic postural stability during walking. Although MoS is typically computed from optical motion capture data, previous studies have shown that MoS can be approximated from six-axis kinematic signals-linear acceleration and angular velocity-measured by inertial measurement units (IMUs). With IMU-equipped devices such as smartphones and smartwatches becoming widespread, it is increasingly common for individuals to carry two or more such devices in daily life. This study aimed to identify combinations of two body locations that most effectively predict MoS. IMU sensors were attached to ten body locations while participants walked on a treadmill. Principal motion analysis, a reductive regression method for multidimensional time-series data, was employed for MoS prediction, and cross-validation was used for reliable model evaluation. Appropriate combinations of two IMU sensors achieved mean errors of approximately 30 mm and 11 mm in anterior and mediolateral MoS, respectively, compared with reference values derived from optical motion capture. These errors were comparable to the intrinsic standard deviations of MoS, suggesting that IMU-based MoS estimation is sufficiently accurate for the classification of individuals at high fall risk.

Indexed as

GaitPostural BalanceAccelerationAdultBiomechanical PhenomenaFemaleHumansMaleMotion CaptureWalkingmargin of stabilitymotion synergyprincipal motion analysis

Identifiers

PMID41755150
PMCPMC12944258

What OpenQuestion holds

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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.