Evidence map›Paper›PMID 30154992›Full record

ArticleJournal of healthcare engineering2018

An Orthopaedic Robotic-Assisted Rehabilitation Method of the Forearm in Virtual Reality Physiotherapy.

Miguel A Padilla-Castañeda, Edoardo Sotgiu, Michele Barsotti, Antonio Frisoli, Piero Orsini, Alessandro Martiradonna, Cristina Laddaga, Massimo Bergamasco

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06563336 (Effects of the EMG-driven Hand Robot Training in Patients With Hand Dysfunction Due to Nerve Damage From Burns), which is not on this map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 17% 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.

NCT06563336 narecruitingnot on this mapstarted 2023, after this paper: background citation

Effects of the EMG-driven Hand Robot Training in Patients With Hand Dysfunction Due to Nerve Damage From Burns

TypeinterventionalSponsorHangang Sacred Heart HospitalRan2023 to 2024Enrolled60ConditionsBurn Hand, RehabilitationArmsEMG-driven hand robot training, conventional occupational training
3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 34 citations in OpenAlex.

  1. Trial
  2. Review
  3. Article
  4. Article
  5. Article
  6. Impact of industry 4.0 to create advancements in orthopaedics.Journal of clinical orthopaedics and trauma · 2020
    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

8 authors at 2 institutions in 3 countries.

Miguel A Padilla-CastañedaLaboratory of Perceptual Robotics, Pisa, Scuola Superiore Sant' Anna, Italy.ORCID 0000-0002-1389-2074
Edoardo SotgiuLaboratory of Perceptual Robotics, Pisa, Scuola Superiore Sant' Anna, Italy.
Michele BarsottiLaboratory of Perceptual Robotics, Pisa, Scuola Superiore Sant' Anna, Italy.
Antonio FrisoliLaboratory of Perceptual Robotics, Pisa, Scuola Superiore Sant' Anna, Italy.ORCID 0000-0002-7126-4113
Piero OrsiniResearch Center "E.Piaggio", University of Pisa, Pisa, Italy.
Alessandro MartiradonnaAdult Rehabilitation Centre "Luciana Segenni" USL5, Fornacette, Italy.
Cristina LaddagaAdult Rehabilitation Centre "Luciana Segenni" USL5, Fornacette, Italy.
Massimo BergamascoLaboratory of Perceptual Robotics, Pisa, Scuola Superiore Sant' Anna, Italy.
Scuola Superiore Sant'Anna · ITUniversity of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of robotic rehabilitation in orthopaedics has been briefly explored. Despite its possible advantages, the use of computer-assisted physiotherapy of patients with musculoskeletal injuries has received little attention. In this paper, we detailed the development and evaluation of a robotic-assisted rehabilitation system as a new methodology of assisted physiotherapy in orthopaedics. The proposal consists of an enhanced end-effector haptic interface mounted in a passive mechanism for allowing patients to perform upper-limb exercising and integrates virtual reality games conceived explicitly for assisting the treatment of the forearm after injuries at the wrist or elbow joints. The present methodology represents a new approach to assisted physiotherapy for strength and motion recovery of wrist pronation/supination and elbow flexion-extension movements. We design specific game scenarios enriched by proprioceptive and haptic force feedback in three training modes: passive, active, and assisted exercising. The system allows the therapist to tailor the difficulty level on the observed motion capacity of the patients and the kinesiology measurements provided by the system itself. We evaluated the system through the analysis of the muscular activity of two healthy subjects, showing that the system can assign significant working loads during typical physiotherapy treatment profiles. Subsequently, a group of ten patients undergoing manual orthopaedic rehabilitation of the forearm tested the system, under similar conditions at variable intensities. Patients tolerated changes in difficulty through the tests, and they expressed a favourable opinion of the system through the administered questionnaires, which indicates that the system was well accepted and that the proposed methodology was feasible for the case study for subsequently controlled trials. Finally, a predictive model of the performance score in the form of a linear combination of kinesiology observations was implemented in function of difficult training parameters, as a way of systematically individualising the training during the therapy, for subsequent studies.

Indexed as

RoboticsVirtual RealityAdultAgedAged, 80 and overCalibrationComputersElbow JointEquipment DesignExerciseFeedbackFemaleHumansMaleMiddle AgedOrthopedics

Identifiers

PMID30154992
PMCPMC6093033
OpenAlexW2886281699

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.