Evidence map›Paper›PMID 34962969›Full record

ArticlePloS one2021

Enhanced location tracking in sensor fusion-assisted virtual reality micro-manipulation environments.

John David Prieto Prada, Jintaek Im, Hyondong Oh, Cheol Song

Abstract read
In one paragraph

Article in PloS one, 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
–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.

John David Prieto PradaDepartment of Robotics Engineering, DGIST, Daegu, South Korea.
Jintaek ImDepartment of Robotics Engineering, DGIST, Daegu, South Korea.
Hyondong OhSchool of Mechanical, Aerospace and Nuclear Engineering, UNIST, Ulsan, South Korea.
Cheol SongDepartment of Robotics Engineering, DGIST, Daegu, South Korea.ORCID 0000-0003-1909-9816

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual reality (VR) technology plays a significant role in many biomedical applications. These VR scenarios increase the valuable experience of tasks requiring great accuracy with human subjects. Unfortunately, commercial VR controllers have large positioning errors in a micro-manipulation task. Here, we propose a VR-based framework along with a sensor fusion algorithm to improve the microposition tracking performance of a microsurgical tool. To the best of our knowledge, this is the first application of Kalman filter in a millimeter scale VR environment, by using the position data between the VR controller and an inertial measuring device. This study builds and tests two cases: (1) without sensor fusion tracking and (2) location tracking with active sensor fusion. The static and dynamic experiments demonstrate that the Kalman filter can provide greater precision during micro-manipulation in small scale VR scenarios.

Indexed as

Man-Machine SystemsUser-Computer InterfaceVirtual RealityAdultAlgorithmsComputersEquipment DesignFemaleHumansMaleMicromanipulationMovementReproducibility of ResultsSoftware

Identifiers

PMID34962969
PMCPMC8714085

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.