Evidence map›Paper›PMID 29686644›Full record

ArticleFrontiers in neurology2018

Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond.

Marianna Semprini, Matteo Laffranchi, Vittorio Sanguineti, Laura Avanzino, Roberto De Icco, Lorenzo De Michieli, Michela Chiappalone

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04304976 (Clinical Trial ROBERT® - Project Active Training), which is not on this map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
–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.

NCT04304976 nacompletednot on this mapstarted 2019, after this paper: background citation

Clinical Trial ROBERT® - Project Active Training

TypeinterventionalSponsorLifescience-RoboticsRan2019 to 2020Enrolled8ConditionsApoplexy, Stroke, Spinal Cord Injuries, SafetyArmsROBERT®
3 · Its place in the literature

Who cites it

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. Article
  6. Article
  7. Brain-Computer Interfaces for Stroke Motor Rehabilitation.Bioengineering (Basel, Switzerland) · 2025
    Review
  8. Article
  9. Review
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  11. Enabling Model-Based Design for Real-Time Spike Detection.IEEE open journal of engineering in medicine and biology · 2025
    Article
  12. Review
  13. Article
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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

7 authors.

Marianna SempriniRehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
Matteo LaffranchiRehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
Vittorio SanguinetiDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy.
Laura AvanzinoSection of Human Physiology, Department of Experimental Medicine (DIMES), University of Genova, Genova, Italy.
Roberto De IccoDepartment of Neurology and Neurorehabilitation, Istituto Neurologico Nazionale C. Mondino, Pavia, Italy.
Lorenzo De MichieliRehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
Michela ChiappaloneRehab Technologies, Istituto Italiano di Tecnologia, Genova, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Indexed as

brain–computer interfacemotor impairmentneurologic disorderneuromodulationpersonalization

Identifiers

PMID29686644
PMCPMC5900382

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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.