Evidence map›Paper›PMID 37631637›Full record

ReviewSensors (Basel, Switzerland)2023

Home-Based Rehabilitation of the Shoulder Using Auxiliary Systems and Artificial Intelligence: An Overview.

Bruno Cunha, Ricardo Ferreira, Andreia S P Sousa

Open access · goldAbstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
4.5field-weighted citation impact, top 5% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.

  1. Pooled it
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  4. Review
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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

3 authors at 2 institutions in 1 country.

Bruno CunhaCenter for Rehabilitation Research-Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal.ORCID 0000-0002-8661-3080
Ricardo FerreiraInstitute for Systems and Computer Engineering, Technology and Science-Telecommunications and Multimedia Centre, FEUP, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.ORCID 0000-0003-0494-6752
Andreia S P SousaCenter for Rehabilitation Research-Human Movement System (Re)habilitation Area, Department of Physiotherapy, School of Health-Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 400, 4200-072 Porto, Portugal.ORCID 0000-0001-9528-1463
Polytechnic Institute of Porto · PTUniversidade do Porto · PT

Funding

Fundação para a Ciência e Tecnologia UIDB/05210/2020Fundo Europeu de Desenvolvimento Regional (FEDER) NORTE-01-0145-FEDER-000045
6 · The paper itself

Abstract

Advancements in modern medicine have bolstered the usage of home-based rehabilitation services for patients, particularly those recovering from diseases or conditions that necessitate a structured rehabilitation process. Understanding the technological factors that can influence the efficacy of home-based rehabilitation is crucial for optimizing patient outcomes. As technologies continue to evolve rapidly, it is imperative to document the current state of the art and elucidate the key features of the hardware and software employed in these rehabilitation systems. This narrative review aims to provide a summary of the modern technological trends and advancements in home-based shoulder rehabilitation scenarios. It specifically focuses on wearable devices, robots, exoskeletons, machine learning, virtual and augmented reality, and serious games. Through an in-depth analysis of existing literature and research, this review presents the state of the art in home-based rehabilitation systems, highlighting their strengths and limitations. Furthermore, this review proposes hypotheses and potential directions for future upgrades and enhancements in these technologies. By exploring the integration of these technologies into home-based rehabilitation, this review aims to shed light on the current landscape and offer insights into the future possibilities for improving patient outcomes and optimizing the effectiveness of home-based rehabilitation programs.

Indexed as

Augmented RealityShoulderArtificial IntelligenceHumansMachine LearningSoftwareaugmented realityexoskeletonshome-based rehabilitationmachine learningrobotsserious gamesvirtual realitywearables

Identifiers

PMID37631637
PMCPMC10459225
OpenAlexW4385759237

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.