↓ Skip to main content

A muscle-driven approach to restore stepping with an exoskeleton for individuals with paraplegia

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, May 2017
Altmetric Badge

Mentioned by

twitter
1 X user

Citations

dimensions_citation
35 Dimensions

Readers on

mendeley
184 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A muscle-driven approach to restore stepping with an exoskeleton for individuals with paraplegia
Published in
Journal of NeuroEngineering and Rehabilitation, May 2017
DOI 10.1186/s12984-017-0258-6
Pubmed ID
Authors

Sarah R. Chang, Mark J. Nandor, Lu Li, Rudi Kobetic, Kevin M. Foglyano, John R. Schnellenberger, Musa L. Audu, Gilles Pinault, Roger D. Quinn, Ronald J. Triolo

Abstract

Functional neuromuscular stimulation, lower limb orthosis, powered lower limb exoskeleton, and hybrid neuroprosthesis (HNP) technologies can restore stepping in individuals with paraplegia due to spinal cord injury (SCI). However, a self-contained muscle-driven controllable exoskeleton approach based on an implanted neural stimulator to restore walking has not been previously demonstrated, which could potentially result in system use outside the laboratory and viable for long term use or clinical testing. In this work, we designed and evaluated an untethered muscle-driven controllable exoskeleton to restore stepping in three individuals with paralysis from SCI. The self-contained HNP combined neural stimulation to activate the paralyzed muscles and generate joint torques for limb movements with a controllable lower limb exoskeleton to stabilize and support the user. An onboard controller processed exoskeleton sensor signals, determined appropriate exoskeletal constraints and stimulation commands for a finite state machine (FSM), and transmitted data over Bluetooth to an off-board computer for real-time monitoring and data recording. The FSM coordinated stimulation and exoskeletal constraints to enable functions, selected with a wireless finger switch user interface, for standing up, standing, stepping, or sitting down. In the stepping function, the FSM used a sensor-based gait event detector to determine transitions between gait phases of double stance, early swing, late swing, and weight acceptance. The HNP restored stepping in three individuals with motor complete paralysis due to SCI. The controller appropriately coordinated stimulation and exoskeletal constraints using the sensor-based FSM for subjects with different stimulation systems. The average range of motion at hip and knee joints during walking were 8.5°-20.8° and 14.0°-43.6°, respectively. Walking speeds varied from 0.03 to 0.06 m/s, and cadences from 10 to 20 steps/min. A self-contained muscle-driven exoskeleton was a feasible intervention to restore stepping in individuals with paraplegia due to SCI. The untethered hybrid system was capable of adjusting to different individuals' needs to appropriately coordinate exoskeletal constraints with muscle activation using a sensor-driven FSM for stepping. Further improvements for out-of-the-laboratory use should include implantation of plantar flexor muscles to improve walking speed and power assist as needed at the hips and knees to maintain walking as muscles fatigue.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 184 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 184 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 27 15%
Student > Bachelor 26 14%
Student > Master 22 12%
Researcher 16 9%
Student > Doctoral Student 9 5%
Other 30 16%
Unknown 54 29%
Readers by discipline Count As %
Engineering 49 27%
Nursing and Health Professions 19 10%
Medicine and Dentistry 19 10%
Neuroscience 8 4%
Psychology 6 3%
Other 18 10%
Unknown 65 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 June 2017.
All research outputs
#18,552,700
of 22,977,819 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#993
of 1,289 outputs
Outputs of similar age
#241,115
of 316,105 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#24
of 31 outputs
Altmetric has tracked 22,977,819 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,289 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 316,105 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.