ArticleDigital health
Global trends in wearable sensors for stroke motor rehabilitation: A bibliometric analysis.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Stroke is a leading cause of long-term disability, and wearable technologies have emerged as promising tools in motor rehabilitation. This study presents a bibliometric and visual analysis of global research on wearable devices for stroke motor recovery, aiming to map knowledge structures, identify research hotspots, and reveal emerging trends. Methods: A total of 564 English-language publications from 2005 to June 2025 were retrieved from the Web of Science Core Collection, with trend and burst analyses conducted through 2024. Using CiteSpace, VOSviewer, RStudio, and OriginPro, we analyzed publication trends, country and institutional contributions, author collaboration, co-citation networks, keyword co-occurrence, clustering, and burst terms. Results: Over the past two decades, the number of publications has increased steadily, with the United States and China being the most productive. Core themes include gait analysis, upper-limb recovery, and sensor-based monitoring, while recent bursts highlight the growing exploration of data-driven and AI-assisted approaches to personalized rehabilitation. Conclusion: This study provides a comprehensive overview of research development in this domain and offers insight for future interdisciplinary and data-driven rehabilitation innovations.
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What OpenQuestion holds
Registered trials
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.