Evidence map›Paper›PMID 30594257›Full record

ArticleJournal of neuroengineering and rehabilitation2018

User-centered practicability analysis of two identification strategies in electrode arrays for FES induced hand motion in early stroke rehabilitation.

Christina Salchow-Hömmen, Natalie Jankowski, Markus Valtin, Laura Schönijahn, Sebastian Böttcher, Frank Dähne, Thomas Schauer

Open access · goldAbstract read
In one paragraph

Article 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. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 31% 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

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Trial
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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 at 3 institutions in 1 country.

Christina Salchow-HömmenControl Systems Group, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany. salchow@control.tu-berlin.de.ORCID 0000-0001-5527-9895
Natalie JankowskiInstitut für Rehabilitationswissenschaften, Humboldt Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany.
Markus ValtinControl Systems Group, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany.
Laura SchönijahnInstitut für Rehabilitationswissenschaften, Humboldt Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany.
Sebastian BöttcherKlinik für Neurologie mit Stroke Unit und Frührehabilitation, Unfallkrankenhaus Berlin, Warener Str. 7, Berlin, 12683, Germany.
Frank DähneKlinik für Neurologie mit Stroke Unit und Frührehabilitation, Unfallkrankenhaus Berlin, Warener Str. 7, Berlin, 12683, Germany.
Thomas SchauerControl Systems Group, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany.
Technische Universität Berlin · DEHumboldt-Universität zu Berlin · DEUnfallkrankenhaus Berlin · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSurface electrode arrays have become popular in the application of functional electrical stimulation (FES) on the forearm. Arrays consist of multiple, small elements, which can be activated separately or in groups, forming virtual electrodes (VEs). As technology progress yields rising numbers of possible elements, an effective search strategy for suitable VEs in electrode arrays is of increasing importance. Current methods can be time-consuming, lack user integration, and miss an evaluation regarding clinical acceptance and practicability.

methodsTwo array identification procedures with different levels of user integration-a semi-automatic and a fully automatic approach-are evaluated. The semi-automatic method allows health professionals to continuously modify VEs via a touchscreen while the stimulation intensities are automatically controlled to maintain sufficient wrist extension. The automatic approach evaluates stimulation responses of various VEs for different intensities using a cost function and joint-angles recordings. Both procedures are compared in a clinical setup with five sub-acute stroke patients with moderate hand disabilities. The task was to find suitable VEs in two arrays with 59 elements in total to generate hand opening and closing for a grasp-and-release task. Practicability and acceptance by patients and health professionals were investigated using questionnaires and interviews.

resultsBoth identification methods yield suitable VEs for hand opening and closing in patients who could tolerate the stimulation. However, the resulting VEs differed for both approaches. The average time for a complete search was 25% faster for the semi-automatic approach (semi-automatic: 7.3min, automatic: 10.5min). User acceptance was high for both methods, while no clear preference could be identified.

conclusionsThe semi-automatic approach should be preferred as the search strategy in arrays on the forearm. The observed faster search duration will further reduce when applying the system repeatedly on a patient as only small position adjustments for VEs are required. However, the setup time will significantly increase for generation of various grasp types and adaptation to different arm postures. We recommend different levels of user integration in FES systems such that the search strategy can be chosen based on the users' preferences and application scenario.

Indexed as

AlgorithmsAutomationElectric Stimulation TherapyFemaleHumansMaleMiddle AgedStroke RehabilitationElectrode arrayFunctional electrical stimulationHand rehabilitationIdentificationStrokeUser-centered designVirtual electrodes

Identifiers

PMID30594257
PMCPMC6310929
OpenAlexW2907310258

What OpenQuestion holds

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LicenceCC BY
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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.