Evidence map›Paper›PMID 42181301›Full record

ArticleData in brief2026

An upper limb stroke rehabilitation exercise video dataset.

S Nandana, M Thenmozhi Dharshini, Vismaya Viswanathan, Binoy B Nair

Abstract read
In one paragraph

Article in Data in brief, 2026. 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.

S NandanaDepartment of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
M Thenmozhi DharshiniDepartment of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Vismaya ViswanathanDepartment of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Binoy B NairDepartment of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke is one of the leading causes of disability worldwide with a disproportionately high burden in low and middle-income countries. In such countries, limited access to rehabilitation centres, shortage of trained physiotherapists and socioeconomic constraints impede continuous post-stroke care resulting in poorer recovery. Telerehabilitation has emerged as a scalable and cost-effective solution enabling remote monitoring and therapy personalization. Robust vision-based rehabilitation exercise assessment systems can play a significant role in such telerehabilitation programs. This article presents a well-annotated, exercise-specific video dataset consisting of 491 videos representing four distinct upper limb muscular strengthening exercises recommended for home rehabilitation performed by ten volunteers with the data captured using conventional RGB cameras at 30 frames per second under different background and lighting conditions. This dataset can be used to develop deep learning based low-cost telerehabilitation systems enabling availability of better post-stroke care for the patients from economically disadvantaged backgrounds.

Indexed as

ArmDeep learningHandHemiplegiaStrokeTelerehabilitation

Identifiers

PMID42181301
PMCPMC13195782

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.