Evidence map›Paper›PMID 29692699›Full record

ArticleFrontiers in neuroscience2018

A Control Scheme That Uses Dynamic Postural Synergies to Coordinate a Hybrid Walking Neuroprosthesis: Theory and Experiments.

Naji A Alibeji, Vahidreza Molazadeh, Brad E Dicianno, Nitin Sharma

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04453943 (Modeling, Optimization, and Control Methods for a Personalized Hybrid Walking Exoskeleton), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 9% 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.

NCT04453943 naunknown statusnot on this mapstarted 2020, after this paper: background citation

Modeling, Optimization, and Control Methods for a Personalized Hybrid Walking Exoskeleton

TypeinterventionalSponsorNorth Carolina State UniversityRan2020 to 2023Enrolled30ConditionsSpinal Cord Injuries, Paraplegia, Spinal, Paraplegia, IncompleteArmsExoskeleton Walking - with or without FES, Exoskeleton Sitting to Standing - with or without FES
3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.

  1. Pooled it
  2. Article
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  4. An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2022
    Article
  5. Article
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  7. A Tube-based Model Predictive Control Method to Regulate a Knee Joint with Functional Electrical Stimulation and Electric Motor Assist.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2021
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  10. Review
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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

4 authors at 2 institutions in 1 country.

Naji A AlibejiDepartment of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States.
Vahidreza MolazadehDepartment of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, United States.
Brad E DiciannoDepartment of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, United States.
Nitin SharmaDepartment of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, United States.
University of Pittsburgh · USCase Western Reserve University · US

Funding

Control of FES and an Electric Motor Drive for a hybrid gait neuroprosthesisR03HD086529 · NICHD · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SHARMA, NITIN · 2015 to 2016
$140k
NICHD NIH HHS R03 HD086529
6 · The paper itself

Abstract

A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury.

Indexed as

hybrid neuroprosthesisLyapunovneuromuscular stimulationnonlinear controlsynergy

Identifiers

PMID29692699
PMCPMC5902565
OpenAlexW2797832156

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.