Evidence map›Paper›PMID 37593414›Full record

ArticleMethodsX2023

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home.

Prashant K Jamwal, Aibek Niyetkaliyev, Shahid Hussain, Aditi Sharma, Paulette Van Vliet

Open access · goldAbstract read
In one paragraph

Article in MethodsX, 2023. 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
1.0field-weighted citation impact, top 23% of its field
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, 7 citations in OpenAlex.

  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

5 authors at 3 institutions in 2 countries.

Prashant K JamwalDepartment of Electrical and Computer Engineering, Nazarbayev University, Astana, Kazakhstan.
Aibek NiyetkaliyevDepartment of Robotics Engineering, Nazarbayev University, Astana, Kazakhstan.
Shahid HussainSchool of Information Technology and Systems, University of Canberra, Canberra, ACT, Australia.
Aditi SharmaDepartment of Electrical and Computer Engineering, Nazarbayev University, Astana, Kazakhstan.
Paulette Van VlietResearch and Innovation Division, The University of Newcastle, NSW, Australia.
Nazarbayev University · KZUniversity of Canberra · AUUniversity of Newcastle Australia · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are

Indexed as

Internet of thingsInverse kinematicsMachine learningStrokeUsing the internet of robotics things (IoRT) framework for home-based upper limb rehabilitationVisual servoing

Identifiers

PMID37593414
PMCPMC10428111
OpenAlexW4385494218

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.