Evidence map›Paper›PMID 39744590›Full record

ArticleIEEE robotics and automation letters2024

Remote Extended Reality with Markerless Motion Tracking for Sitting Posture Training.

Xupeng Ai, Sunil K Agrawal

Abstract read
In one paragraph

Article in IEEE robotics and automation letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Xupeng AiDepartment of Mechanical Engineering, Columbia University, New York, NY, 10027, USA.
Sunil K AgrawalDepartment of Mechanical Engineering, Columbia University, New York, NY, 10027, USA.; Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, 10027, USA.

Funding

Improving seated postural control and upper extremity function in bilateral CP with a robotic Trunk-Support-Trainer (TruST)R01HD101903 · NICHD · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI AGRAWAL, SUNIL K., GORDON, ANDREW M. · 2020 to 2024
$3.1M
NICHD NIH HHS R01 HD101903
6 · The paper itself

Abstract

Dynamic postural control during sitting is essential for functional mobility and daily activities. Extended reality (XR) presents a promising solution for posture training in addressing conventional training limitations related to patient accessibility and ecological validity. We developed a remote XR rehabilitation system with markerless motion tracking for sitting posture training. Forty-two healthy subjects participated in this proof-of-concept pilot study. Each subject completed 24 rounds of multi-directional reach tasks using the system and 24 rounds without it. Motion data were collected via online meetings using built-in camera in the user's laptop. Functional reach test scores were analyzed to assess the impact of the system on motor performance. Four standard questionnaires were used to assess the effects of this system on presence, simulator sickness, engagement, and enjoyment. Our results indicate that the remote XR training system significantly improved functional reach performance and proved highly effective for telerehabilitation. XR interaction also enhanced training engagement and enjoyment. By bridging the spatial gap between patients and therapists, this system enables personalized and engaging home-based intervention. Additionally, it facilitates more natural movements by eliminating body marker constraints and laboratory limitations. This study should serve as a stepping stone to advancing novel remote XR rehabilitation systems.

Indexed as

Markerless Motion TrackingPostural ControlRemote Rehabilitation SystemVirtual Reality and Interfaces

Identifiers

PMID39744590
PMCPMC11687359

What Socratic holds

Textmetadata
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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 Socratic graph.