Evidence map›Paper›PMID 34987303›Full record

ReviewRehabilitation process and outcome2021

The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation.

Thomas Hellsten, Jonny Karlsson, Muhammed Shamsuzzaman, Göran Pulkkis

Abstract readReview
In one paragraph

Review in Rehabilitation process and outcome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

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  10. Observational
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  12. Artificial Intelligence Applications in Musculoskeletal Imaging.Current reviews in musculoskeletal medicine · 2025
    Review
  13. Article
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  16. A DICOM-based standard for quantitative physical rehabilitation.Computational and structural biotechnology journal · 2025
    Article
  17. Article
  18. Empowering sickle cell disease care: the rise ofFrontiers in rehabilitation sciences · 2024
    Article
  19. Review
  20. 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.

Thomas HellstenDepartment of Health and Wellbeing, Arcada University of Applied Sciences, Helsinki, Finland.
Jonny KarlssonDepartment of Business Administration and Analytics, Arcada University of Applied Sciences, Helsinki, Finland.
Muhammed ShamsuzzamanDepartment of Business Administration and Analytics, Arcada University of Applied Sciences, Helsinki, Finland.
Göran PulkkisDepartment of Business Administration and Analytics, Arcada University of Applied Sciences, Helsinki, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral factors, including the aging population and the recent corona pandemic, have increased the need for cost effective, easy-to-use and reliable telerehabilitation services. Computer vision-based marker-less human pose estimation is a promising variant of telerehabilitation and is currently an intensive research topic. It has attracted significant interest for detailed motion analysis, as it does not need arrangement of external fiducials while capturing motion data from images. This is promising for rehabilitation applications, as they enable analysis and supervision of clients' exercises and reduce clients' need for visiting physiotherapists in person. However, development of a marker-less motion analysis system with precise accuracy for joint identification, joint angle measurements and advanced motion analysis is an open challenge.

objectivesThe main objective of this paper is to provide a critical overview of recent computer vision-based marker-less human pose estimation systems and their applicability for rehabilitation application. An overview of some existing marker-less rehabilitation applications is also provided.

methodsThis paper presents a critical review of recent computer vision-based marker-less human pose estimation systems with focus on their provided joint localization accuracy in comparison to physiotherapy requirements and ease of use. The accuracy, in terms of the capability to measure the knee angle, is analysed using simulation.

resultsCurrent pose estimation systems use 2D, 3D, multiple and single view-based techniques. The most promising techniques from a physiotherapy point of view are 3D marker-less pose estimation based on a single view as these can perform advanced motion analysis of the human body while only requiring a single camera and a computing device. Preliminary simulations reveal that some proposed systems already provide a sufficient accuracy for 2D joint angle estimations.

conclusionsEven though test results of different applications for some proposed techniques are promising, more rigour testing is required for validating their accuracy before they can be widely adopted in advanced rehabilitation applications.

Indexed as

Computer visionmarker-lessmotion analysistelerehabilitation

Identifiers

PMID34987303
PMCPMC8492027

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Registered trials

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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.