Evidence map›Paper›PMID 29298691›Full record

ArticleJournal of neuroengineering and rehabilitation2018

Volition-adaptive control for gait training using wearable exoskeleton: preliminary tests with incomplete spinal cord injury individuals.

Vijaykumar Rajasekaran, Eduardo López-Larraz, Fernando Trincado-Alonso, Joan Aranda, Luis Montesano, Antonio J Del-Ama, Jose L Pons

Open access · goldAbstract read
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.6field-weighted citation impact, top 19% 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.

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

11 citing papers in PubMed, 28 citations in OpenAlex.

  1. Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation.Measurement : journal of the International Measurement Confederation · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. 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 at 6 institutions in 3 countries.

Vijaykumar RajasekaranSchool of Computer Science, University of Birmingham, Birmingham, UK. v.rajasekaran@bham.ac.uk.ORCID 0000-0002-9236-8384
Eduardo López-LarrazInstitute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany.
Fernando Trincado-AlonsoBiomechanics and Technical Aids unit, National Hospital for Spinal cord injury, Toledo, Spain.
Joan ArandaDepartamento de Automatic e Control, Universitat Politécnica de Catalunya, Barcelona-Tech, Barcelona, Spain.
Luis MontesanoDepartamento de Informatica e Ingenería de Sistemas and, Instituto de Investigacion en Ingeneria de Aragon (I3A), University of Zaragoza, Zaragoza, Spain.
Antonio J Del-AmaBiomechanics and Technical Aids unit, National Hospital for Spinal cord injury, Toledo, Spain.
Jose L PonsNeural Rehabilitation group, Spanish National Research Council (CSIC), Cajal Institute, Madrid, Spain.
Hospital Nacional de Parapléjicos · ESConsejo Superior de Investigaciones Científicas · ESUniversidad de Zaragoza · ESUniversitat Politècnica de Catalunya · ESUniversity of Birmingham · GBUniversity of Tübingen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGait training for individuals with neurological disorders is challenging in providing the suitable assistance and more adaptive behaviour towards user needs. The user specific adaptation can be defined based on the user interaction with the orthosis and by monitoring the user intentions. In this paper, an adaptive control model, commanded by the user intention, is evaluated using a lower limb exoskeleton with incomplete spinal cord injury individuals (SCI).

methodsA user intention based adaptive control model has been developed and evaluated with 4 incomplete SCI individuals across 3 sessions of training per individual. The adaptive control model modifies the joint impedance properties of the exoskeleton as a function of the human-orthosis interaction torques and the joint trajectory evolution along the gait sequence, in real time. The volitional input of the user is identified by monitoring the neural signals, pertaining to the user's motor activity. These volitional inputs are used as a trigger to initiate the gait movement, allowing the user to control the initialization of the exoskeleton movement, independently. A Finite-state machine based control model is used in this set-up which helps in combining the volitional orders with the gait adaptation.

resultsThe exoskeleton demonstrated an adaptive assistance depending on the patients' performance without guiding them to follow an imposed trajectory. The exoskeleton initiated the trajectory based on the user intention command received from the brain machine interface, demonstrating it as a reliable trigger. The exoskeleton maintained the equilibrium by providing suitable assistance throughout the experiments. A progressive change in the maximum flexion of the knee joint was observed at the end of each session which shows improvement in the patient performance. Results of the adaptive impedance were evaluated by comparing with the application of a constant impedance value. Participants reported that the movement of the exoskeleton was flexible and the walking patterns were similar to their own distinct patterns.

conclusionsThis study demonstrates that user specific adaptive control can be applied on a wearable robot based on the human-orthosis interaction torques and modifying the joints' impedance properties. The patients perceived no external or impulsive force and felt comfortable with the assistance provided by the exoskeleton. The main goal of such a user dependent control is to assist the patients' needs and adapt to their characteristics, thus maximizing their engagement in the therapy and avoiding slacking. In addition, the initiation directly controlled by the brain allows synchronizing the user's intention with the afferent stimulus provided by the movement of the exoskeleton, which maximizes the potentiality of the system in neuro-rehabilitative therapies.

Indexed as

Brain-Computer InterfacesExoskeleton DeviceVolitionAdultAlgorithmsFemaleGaitHumansIntentionLower ExtremityMaleMiddle AgedSpinal Cord InjuriesYoung AdultAdaptive controlBrain-machine interfaceExoskeletonFinite-state machineGait trainingIncomplete SCIVolitional control

Identifiers

PMID29298691
PMCPMC5751847
OpenAlexW2781771579

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.