Evidence map›Paper›PMID 41847625›Full record

ArticleFrontiers in sports and active living2026

Exploring the potential of augmented reality in rehabilitation: a novel approach for post-stroke unilateral neglect.

Ming Yanzhen, Rao Yunhua, Chen Song, Zeng Heqiong, Yang Zufang, Alan Wang

Abstract read
In one paragraph

Article in Frontiers in sports and active living, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

6 authors.

Ming YanzhenSchool of Artificial Intelligence Academy, Wuhan Technology and Business University, Wuhan, China.
Rao YunhuaElectronic Information School, Wuhan University, Wuhan, China.
Chen SongAI Research Institute, Langxin Technology Group Co. Ltd., Wuhan, Hubei, China.
Zeng HeqiongSchool of Artificial Intelligence Academy, Wuhan Technology and Business University, Wuhan, China.
Yang ZufangSchool of Artificial Intelligence Academy, Wuhan Technology and Business University, Wuhan, China.
Alan WangAuckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Unilateral neglect affects up to 80% of right hemisphere stroke survivors and poses a significant barrier to rehabilitation. It is a strong predictor of poor prognosis and leads to prolonged hospital stays, yet no established treatment currently exists. Methods: We propose an innovative approach, NeuroNavAR, a novel treatment method that utilizes neural network models to train the human nervous system under the guidance of augmented reality (AR). This method focuses on an AR-enhanced smart training program specifically for post-stroke spatial neglect. While the current implementation runs on a GPU server for technical validation, the application is designed for future deployment on Android and iOS devices. It employs image processing algorithms to identify real-world objects, such as clocks and chairs, segment their contours, and generate virtual bee patterns. These virtual bees navigate along the contours of the objects, and patients are encouraged to follow the bee patterns, receiving rewards upon completing a cycle. If patients fail to complete the cycle, virtual birds are introduced into their field of view to guide them back to the bee patterns, thereby enhancing engagement. Results: This study reports only on the technical feasibility of the system. No clinical, behavioral, or usability data were collected. On a test dataset comprising 537 indoor images from the ADE20K dataset (accessed June 2024), the segmentation achieved a mean Intersection over Union (IoU) of 76.70% (SD = 8.2%) and an accuracy of 88.51% (SD = 4.7%). Conclusion: The NeuroNavAR program represents a preclinical engineering feasibility study that provides a more intuitive and user-friendly training experience compared to traditional rehabilitation methods. Its innovative use of augmented reality and real-time feedback is designed to support, but has not yet demonstrated, improved rehabilitation outcomes for patients with unilateral spatial neglect. Clinical validation is planned for 2026.

Indexed as

augmented realityneural networkNeuroNavARpost-strokesegmentationunilateral neglect

Identifiers

PMID41847625
PMCPMC12989549

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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.