Evidence map›Paper›PMID 34640945›Full record

ArticleSensors (Basel, Switzerland)2021

Head Trajectory Diagrams for Gait Symmetry Analysis Using a Single Head-Worn IMU.

Tong-Hun Hwang, Alfred O Effenberg

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 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
4.1field-weighted citation impact, top 6% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. 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

2 authors at 1 institution in 1 country.

Tong-Hun HwangInstitute of Sports Science, Leibniz University Hannover, 30167 Hannover, Germany.ORCID 0000-0002-4454-0055
Alfred O EffenbergInstitute of Sports Science, Leibniz University Hannover, 30167 Hannover, Germany.
Leibniz University Hannover · DE

Funding

Horizon 2020 641321
6 · The paper itself

Abstract

Gait symmetry analysis plays an important role in the diagnosis and rehabilitation of pathological gait. Recently, wearable devices have also been developed for simple gait analysis solutions. However, measurement in clinical settings can differ from gait in daily life, and simple wearable devices are restricted to a few parameters, providing one-sided trajectories of one arm or leg. Therefore, head-worn devices with sensors (e.g., earbuds) should be considered to analyze gait symmetry because the head sways towards the left and right side depending on steps. This paper proposed new visualization methods using head-worn sensors, able to facilitate gait symmetry analysis outside as well as inside. Data were collected with an inertial measurement unit (IMU) based motion capture system when twelve participants walked on the 400-m running track. From head trajectories on the transverse and frontal plane, three types of diagrams were displayed, and five concepts of parameters were measured for gait symmetry analysis. The mean absolute percentage error (MAPE) of step counting was lower than 0.65%, representing the reliability of measured parameters. The methods enable also left-right step recognition (MAPE ≤ 2.13%). This study can support maintenance and relearning of a balanced healthy gait in various areas with simple and easy-to-use devices.

Indexed as

Gait AnalysisWalkingGaitHeadHumansReproducibility of Resultseye diagramgait symmetrygait symmetry analysishead-worn sensorinertial measurement unitwearable sensor

Identifiers

PMID34640945
PMCPMC8512482
OpenAlexW3206561043

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.