Evidence map›Paper›PMID 38645661›Full record

ArticleComputers & graphics2024

Visual Feedback and Guided Balance Training in an Immersive Virtual Reality Environment for Lower Extremity Rehabilitation.

Sydney Segear, Vuthea Chheang, Lauren Baron, Jicheng Li, Kangsoo Kim, Roghayeh Leila Barmaki

Abstract read
In one paragraph

Article in Computers & graphics, 2024. 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
–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

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

  1. Pooled it
  2. Review
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

6 authors.

Sydney SegearDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
Vuthea ChheangCenter of Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, USA.
Lauren BaronDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
Jicheng LiDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA.
Kangsoo KimDepartment of Electrical and Software Engineering, University of Calgary, Alberta, Canada.
Roghayeh Leila BarmakiDepartment of Computer and Information Sciences, University of Delaware, Newark, DE, USA.

Funding

Predictive Modeling & Optimal Control Framework for Model-Based Epidemic Response in DelawareP20GM103446 · NIGMS · UNIVERSITY OF DELAWARE · PI Shawn W Polson · 2012 to 2026
$67.2M
NIGMS NIH HHS P20 GM103446
6 · The paper itself

Abstract

Balance training is essential for physical rehabilitation procedures, as it can improve functional mobility and enhance cognitive coordination. However, conventional balance training methods may have limitations in terms of motivation, real-time objective feedback, and personalization, which a virtual reality (VR) setup may better provide. In this work, we present an immersive VR training environment for lower extremity balance rehabilitation with real-time guidance and feedback. The VR training environment immerses the user in a 3D ice rink model where a virtual coach (agent) leads them through a series of balance poses, and the user controls a trainee avatar with their own movements. We developed two coaching styles: positive-reinforcement and autonomous-supportive, and two viewpoints of the trainee avatar: first-person and third-person. The proposed environment was evaluated in a user study with healthy, non-clinical participants (n = 16, 24.4 ± 5.7 years old, 9 females). Our results show that participants showed stronger performance in the positive-reinforcement style compared to the autonomous-supportive style. Additionally, in the third-person viewpoint, the participants exhibited more stability in the positive-reinforcement style compared to the autonomous-supportive style. For viewpoint, participants exhibited stronger performance in the first-person viewpoint compared to third-person in the autonomous-supportive style, while they were comparable in the positive-reinforcement style. We observed no significant effects on the foot height and number of mistakes. Furthermore, we report the analysis of user performance with balance training poses and subjective measures based on questionnaires to assess the user experience, usability, and task load. The proposed VR balance training could offer an interactive, adaptive, and engaging environment and open new potential research directions for lower extremity rehabilitation.

Indexed as

Balance TrainingGuided VR TrainingHuman-Computer InteractionLower Extremity RehabilitationVirtual Reality

Identifiers

PMID38645661
PMCPMC11031187

What OpenQuestion holds

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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 OpenQuestion graph.