Evidence map›Paper›PMID 34950707›Full record

ReviewFrontiers in robotics and AI2021

Review on Patient-Cooperative Control Strategies for Upper-Limb Rehabilitation Exoskeletons.

Stefano Dalla Gasperina, Loris Roveda, Alessandra Pedrocchi, Francesco Braghin, Marta Gandolla

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 5 pooled it
7.3field-weighted citation impact, top 2% 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

27 citing papers in PubMed, 5 syntheses or guidelines pooled it, 100 citations in OpenAlex.

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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

5 authors at 2 institutions in 2 countries.

Stefano Dalla GasperinaNearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Loris RovedaIstituto Dalle Molle di studi sull'Intelligenza Artificiale (IDSIA), USI-SUPSI, Lugano, Switzerland.
Alessandra PedrocchiNearLab, Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Francesco BraghinWE-COBOT Lab, Polo Territoriale di Lecco, Politecnico di Milano, Lecco, Italy.
Marta GandollaWE-COBOT Lab, Polo Territoriale di Lecco, Politecnico di Milano, Lecco, Italy.
Politecnico di Milano · ITUniversity of Applied Sciences and Arts of Southern Switzerland · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Technology-supported rehabilitation therapy for neurological patients has gained increasing interest since the last decades. The literature agrees that the goal of robots should be to induce motor plasticity in subjects undergoing rehabilitation treatment by providing the patients with repetitive, intensive, and task-oriented treatment. As a key element, robot controllers should adapt to patients' status and recovery stage. Thus, the design of effective training modalities and their hardware implementation play a crucial role in robot-assisted rehabilitation and strongly influence the treatment outcome. The objective of this paper is to provide a multi-disciplinary vision of patient-cooperative control strategies for upper-limb rehabilitation exoskeletons to help researchers bridge the gap between human motor control aspects, desired rehabilitation training modalities, and their hardware implementations. To this aim, we propose a three-level classification based on 1) "high-level" training modalities, 2) "low-level" control strategies, and 3) "hardware-level" implementation. Then, we provide examples of literature upper-limb exoskeletons to show how the three levels of implementation have been combined to obtain a given high-level behavior, which is specifically designed to promote motor relearning during the rehabilitation treatment. Finally, we emphasize the need for the development of compliant control strategies, based on the collaboration between the exoskeleton and the wearer, we report the key findings to promote the desired physical human-robot interaction for neurorehabilitation, and we provide insights and suggestions for future works.

Indexed as

motor recoveryneurorehabilitationphysical human-robot interactionrehabilitation roboticsrobot controlupper-limb exoskeletons

Identifiers

PMID34950707
PMCPMC8688994
OpenAlexW4200401311

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.