Evidence map›Paper›PMID 26341935›Full record

ArticleNeuropsychologia2015

Remapping residual coordination for controlling assistive devices and recovering motor functions.

Camilla Pierella, Farnaz Abdollahi, Ali Farshchiansadegh, Jessica Pedersen, Elias B Thorp, Ferdinando A Mussa-Ivaldi, Maura Casadio

Abstract readCase Reports
In one paragraph

Article in Neuropsychologia, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–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.

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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Data-driven body-machine interface for the accurate control of drones.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  7. Article
  8. Article
  9. Static Versus Dynamic Decoding Algorithms in a Non-Invasive Body-Machine Interface.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2017
    Article
  10. Article
  11. Using noise to shape motor learning.Journal of neurophysiology · 2017
    Article
  12. 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

7 authors.

Camilla PierellaDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy; Department of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA. Electronic address: camilla.pierella@edu.unige.it.
Farnaz AbdollahiDepartment of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
Ali FarshchiansadeghSensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
Jessica PedersenSensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA.
Elias B ThorpSensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
Ferdinando A Mussa-IvaldiDepartment of Physiology, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA; Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL 60611, USA; Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
Maura CasadioDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genova, Italy.

Funding

Motor Learning in a Customized Body-Machine Interface for Persons with ParalysisR01HD072080 · NICHD · REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB · PI MUSSA-IVALDI, FERDINANDO ALESSANDRO · 2012 to 2016
$1.4M
NICHD NIH HHS 1R01HD072080NICHD NIH HHS R01 HD072080
6 · The paper itself

Abstract

The concept of human motor redundancy attracted much attention since the early studies of motor control, as it highlights the ability of the motor system to generate a great variety of movements to achieve any well-defined goal. The abundance of degrees of freedom in the human body may be a fundamental resource in the learning and remapping problems that are encountered in human-machine interfaces (HMIs) developments. The HMI can act at different levels decoding brain signals or body signals to control an external device. The transformation from neural signals to device commands is the core of research on brain-machine interfaces (BMIs). However, while BMIs bypass completely the final path of the motor system, body-machine interfaces (BoMIs) take advantage of motor skills that are still available to the user and have the potential to enhance these skills through their consistent use. BoMIs empower people with severe motor disabilities with the possibility to control external devices, and they concurrently offer the opportunity to focus on achieving rehabilitative goals. In this study we describe a theoretical paradigm for the use of a BoMI in rehabilitation. The proposed BoMI remaps the user's residual upper body mobility to the two coordinates of a cursor on a computer screen. This mapping is obtained by principal component analysis (PCA). We hypothesize that the BoMI can be specifically programmed to engage the users in functional exercises aimed at partial recovery of motor skills, while simultaneously controlling the cursor and carrying out functional tasks, e.g. playing games. Specifically, PCA allows us to select not only the subspace that is most comfortable for the user to act upon, but also the degrees of freedom and coordination patterns that the user has more difficulty engaging. In this article, we describe a family of map modifications that can be made to change the motor behavior of the user. Depending on the characteristics of the impairment of each high-level spinal cord injury (SCI) survivor, we can make modifications to restore a higher level of symmetric mobility (left versus right), or to increase the strength and range of motion of the upper body that was spared by the injury. Results showed that this approach restored symmetry between left and right side of the body, with an increase of mobility and strength of all the degrees of freedom in the participants involved in the control of the interface. This is a proof of concept that our BoMI may be used concurrently to control assistive devices and reach specific rehabilitative goals. Engaging the users in functional and entertaining tasks while practicing the interface and changing the map in the proposed ways is a novel approach to rehabilitation treatments facilitated by portable and low-cost technologies.

Indexed as

Self-Help DevicesUser-Computer InterfaceFemaleHumansLearningMaleMiddle AgedMotor SkillsMusculoskeletal Physiological PhenomenaPrincipal Component AnalysisRecovery of FunctionSpinal Cord InjuriesTreatment OutcomeYoung AdultDimensionality reductionHuman–machine interfaceMotor learningMovement reorganizationRehabilitationSpinal cord injury

Identifiers

PMID26341935
PMCPMC4679682

What OpenQuestion holds

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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.