Evidence map›Paper›PMID 37029432›Full record

ArticleJournal of neuroengineering and rehabilitation2023

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.

Markus Nowak, Raoul M Bongers, Corry K van der Sluis, Alin Albu-Schäffer, Claudio Castellini

Open access · goldAbstract readCase Reports
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 22% 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, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. 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

5 authors at 4 institutions in 2 countries.

Markus NowakInstitute of Robotics and Mechatronics, German Aerospace Center (DLR), Münchner Str. 20, 82234, Weßling, Germany. markus.nowak@dlr.de.
Raoul M BongersDepartment of Human Movement Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Corry K van der SluisDepartment of Rehabilitation Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
Alin Albu-SchäfferInstitute of Robotics and Mechatronics, German Aerospace Center (DLR), Münchner Str. 20, 82234, Weßling, Germany.
Claudio CastelliniInstitute of Robotics and Mechatronics, German Aerospace Center (DLR), Münchner Str. 20, 82234, Weßling, Germany.
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) · DETechnical University of Munich · DEUniversity Medical Center Groningen · NLUniversity of Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMachine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control).

methodsThe participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales.

resultsOver the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study.

conclusionsIncremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.

Indexed as

AmputeesArtificial LimbsExercise TherapyHandMachine LearningElectromyographyFunctional StatusHumansProsthesis DesignRecovery of FunctionReproducibility of ResultsResearch DesignHand prosthesisMachine-learning controlMyocontrolSingle-case experimental designTraining

Identifiers

PMID37029432
PMCPMC10082541
OpenAlexW4362705302

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.