Evidence map›Paper›PMID 32393288›Full record

ArticleJournal of neuroengineering and rehabilitation2020

Guiding functional reorganization of motor redundancy using a body-machine interface.

Dalia De Santis, Ferdinando A Mussa-Ivaldi

Registry-linked trialAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01608438 (Motor Learning in a Customized Body-Machine Interface for Persons With Paralysis), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

NCT01608438 naunknown statusnot on this map

Motor Learning in a Customized Body-Machine Interface for Persons With Paralysis

TypeinterventionalSponsorShirley Ryan AbilityLabRan2013 to 2022Enrolled157ConditionsSpinal Cord InjuryArmsCustomizing the Body-Machine Interface
3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. An Exploratory Multi-Session Study of Learning High-Dimensional Body-Machine Interfacing for Assistive Robot Control.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2023
    Article
  3. A Non-Linear Body Machine Interface for Controlling Assistive Robotic Arms.IEEE transactions on bio-medical engineering · 2023
    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

2 authors.

Dalia De SantisNorthwestern University and the Shirley Ryan AbilityLab, Chicago, IL, USA. dalia.desantis@iit.it.
Ferdinando A Mussa-IvaldiNorthwestern University and the Shirley Ryan AbilityLab, Chicago, IL, USA. sandro@northwestern.edu.

Funding

Human and Machine Learning for Customized Control of Assistive RobotsR01EB024058 · NIBIB · REHABILITATION INSTITUTE OF CHICAGO D/B/A SHIRLEY RYAN ABILITYLAB · PI ARGALL, BRENNA · 2018 to 2024
$1.4M
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
National Institute of Child Health and Human Development (US) 1R01HD072080National Institute on Disability and Rehabilitation Research (US) H133E120010National Institute on Disability, Independent Living, and Rehabilitation Research (US) 90REGE0005-01NIBIB NIH HHS R01 EB024058
6 · The paper itself

Abstract

backgroundBody-machine interfaces map movements onto commands to external devices. Redundant motion signals derived from inertial sensors are mapped onto lower-dimensional device commands. Then, the device users face two problems, a) the structural problem of understanding the operation of the interface and b) the performance problem of controlling the external device with high efficiency. We hypothesize that these problems, while being distinct are connected in that aligning the space of body movements with the space encoded by the interface, i.e. solving the structural problem, facilitates redundancy resolution towards increasing efficiency, i.e. solving the performance problem.

methodsTwenty unimpaired volunteers practiced controlling the movement of a computer cursor by moving their arms. Eight signals from four inertial sensors were mapped onto the two cursor's coordinates on a screen. The mapping matrix was initialized by asking each user to perform free-form spontaneous upper-limb motions and deriving the two main principal components of the motion signals. Participants engaged in a reaching task for 18 min, followed by a tracking task. One group of 10 participants practiced with the same mapping throughout the experiment, while the other 10 with an adaptive mapping that was iteratively updated by recalculating the principal components based on ongoing movements.

resultsParticipants quickly reduced reaching time while also learning to distribute most movement variance over two dimensions. Participants with the fixed mapping distributed movement variance over a subspace that did not match the potent subspace defined by the interface map. In contrast, participant with the adaptive map reduced the difference between the two subspaces, resulting in a smaller amount of arm motions distributed over the null space of the interface map. This, in turn, enhanced movement efficiency without impairing generalization from reaching to tracking.

conclusionsAligning the potent subspace encoded by the interface map to the user's movement subspace guides redundancy resolution towards increasing movement efficiency, with implications for controlling assistive devices. In contrast, in the pursuit of rehabilitative goals, results would suggest that the interface must change to drive the statistics of user's motions away from the established pattern and toward the engagement of movements to be recovered.

trial registrationClinicalTrials.gov, NCT01608438, Registered 16 April 2012.

Indexed as

User-Computer InterfaceAdultFemaleHumansLearningMaleMiddle AgedMovementSelf-Help DevicesYoung AdultBody-machine interfaceForward modelMotor redundancySensorimotor learning

Identifiers

PMID32393288
PMCPMC7216597

What OpenQuestion holds

Textmetadata
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.