Evidence map›Paper›PMID 29866106›Full record

ReviewJournal of neuroengineering and rehabilitation2018

Rehabilitation robots for the treatment of sensorimotor deficits: a neurophysiological perspective.

Roger Gassert, Volker Dietz

2 registry-linked trialsAbstract readReview
In one paragraph

Review in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 127 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
127citing papers in PubMed, 10 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.

NCT04778852 naunknown statusnot on this mapstarted 2020, after this paper: background citation

Quantitative Assessment of Training Effects Using a Wearable Exoskeleton in Parkinson Disease Patients

TypeinterventionalSponsorUniversity of PadovaRan2020 to 2023Enrolled50ConditionsParkinson DiseaseArmsExperimental: EksoGT, Functional kinematic training
NCT07440459 completednot on this mapstarted 2023, after this paper: background citation

Assessing and Modeling the Mechanisms of Recovery During Robot Aided Neurorehabilitation of Walking

TypeobservationalSponsorBioRobotics Group - Spanish National Research Council (CSIC)Ran2023 to 2024Enrolled10ConditionsSpinal Cord Injuries (SCI), Spinal Cord Injury, Neurorehabilitation, Robot Assisted Gait Training
3 · Its place in the literature

Who cites it

127 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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  7. Upper limb soft robotic wearable devices: a systematic review.Journal of neuroengineering and rehabilitation · 2022
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  16. Characterizing the Effects of Adding Virtual and Augmented Reality in Robot-Assisted Training.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024
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67 more citing papers are in PubMed but not listed here.

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

2 authors.

Roger GassertDepartment of Health Sciences and Technology, ETH Zurich, 8092, Zurich, Switzerland. roger.gassert@hest.ethz.ch.ORCID 0000-0002-6373-8518
Volker DietzSpinal Cord Injury Center, Balgrist University Hospital, 8008, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The past decades have seen rapid and vast developments of robots for the rehabilitation of sensorimotor deficits after damage to the central nervous system (CNS). Many of these innovations were technology-driven, limiting their clinical application and impact. Yet, rehabilitation robots should be designed on the basis of neurophysiological insights underlying normal and impaired sensorimotor functions, which requires interdisciplinary collaboration and background knowledge.Recovery of sensorimotor function after CNS damage is based on the exploitation of neuroplasticity, with a focus on the rehabilitation of movements needed for self-independence. This requires a physiological limb muscle activation that can be achieved through functional arm/hand and leg movement exercises and the activation of appropriate peripheral receptors. Such considerations have already led to the development of innovative rehabilitation robots with advanced interaction control schemes and the use of integrated sensors to continuously monitor and adapt the support to the actual state of patients, but many challenges remain. For a positive impact on outcome of function, rehabilitation approaches should be based on neurophysiological and clinical insights, keeping in mind that recovery of function is limited. Consequently, the design of rehabilitation robots requires a combination of specialized engineering and neurophysiological knowledge. When appropriately applied, robot-assisted therapy can provide a number of advantages over conventional approaches, including a standardized training environment, adaptable support and the ability to increase therapy intensity and dose, while reducing the physical burden on therapists. Rehabilitation robots are thus an ideal means to complement conventional therapy in the clinic, and bear great potential for continued therapy and assistance at home using simpler devices.This review summarizes the evolution of the field of rehabilitation robotics, as well as the current state of clinical evidence. It highlights fundamental neurophysiological factors influencing the recovery of sensorimotor function after a stroke or spinal cord injury, and discusses their implications for the development of effective rehabilitation robots. It thus provides insights on essential neurophysiological mechanisms to be considered for a successful development and clinical inclusion of robots in rehabilitation.

Indexed as

Exoskeleton DeviceExercise TherapyHumansNeurological RehabilitationRoboticsStroke RehabilitationAssist-as-neededLocomotionNeuroplasticityNeurorehabilitation technologyRobot-assisted therapySensorimotor neurophysiologySpinal cord injuryStrokeUpper limb function

Identifiers

PMID29866106
PMCPMC5987585

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.