Evidence map›Paper›PMID 26054071›Full record

ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2016

Upper Body-Based Power Wheelchair Control Interface for Individuals With Tetraplegia.

Elias B Thorp, Farnaz Abdollahi, David Chen, Ali Farshchiansadegh, Mei-Hua Lee, Jessica P Pedersen, Camilla Pierella, Elliot J Roth, Ismael Seanez Gonzalez, Ferdinando A Mussa-Ivaldi

Abstract read
In one paragraph

Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. A Customizable Fitting Protocol for the Body-Machine Interface.IEEE ... International Conference on Rehabilitation Robotics : [proceedings] · 2025
    Article
  3. Article
  4. Article
  5. An Evolution of Assistive Robot Control to Meet End-User Ability.Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction. ACM/IEEE Conference on Human-Robot Interaction (2024 : Boulder, Colo.) · 2024
    Article
  6. 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
  7. A Non-Linear Body Machine Interface for Controlling Assistive Robotic Arms.IEEE transactions on bio-medical engineering · 2023
    Article
  8. Autoencoder-based myoelectric controller for prosthetic hands.Frontiers in bioengineering and biotechnology · 2023
    Article
  9. Learning to operate a high-dimensional hand via a low-dimensional controller.Frontiers in bioengineering and biotechnology · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Autonomy in Rehabilitation Robotics: An Intersection.Annual review of control, robotics, and autonomous systems · 2018
    Article
  20. 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

10 authors.

Elias B Thorp
Farnaz Abdollahi
David Chen
Ali Farshchiansadegh
Mei-Hua Lee
Jessica P Pedersen
Camilla Pierella
Elliot J Roth
Ismael Seanez Gonzalez
Ferdinando A Mussa-Ivaldi

Funding

PATHOPHYSIOLOGY AND REHABILITATION OF NEURAL DYSFUNCTIONT32HD007418 · NICHD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Levi John Hargrove · 1992 to 2026
$6.5M
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 HD072080NICHD NIH HHS T32 HD007418NICHD NIH HHS T32 HD07418PHS HHS H133E120010
6 · The paper itself

Abstract

Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user's residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional control commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control a power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control.

Indexed as

WheelchairsAdultAlgorithmsBiomechanical PhenomenaFemaleHumansMaleMiddle AgedPractice, PsychologicalQuadriplegiaShoulderSpinal Cord Injuries

Identifiers

PMID26054071
PMCPMC4742425

What OpenQuestion holds

Textmetadata
LicenceTDM
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.