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Motor Learning in a Customized Body-Machine Interface (BMI)

14. November 2019 aktualisiert von: Ferdinando Mussa-Ivaldi, Shirley Ryan AbilityLab

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

People with tetraplegia often retain some level of mobility of the upper body. The proposed study will test the hypothesis that it is possible to develop personalized interfaces, which utilize the residual mobility to enable paralyzed persons to control computers, wheelchairs and other assistive devices. If successful the project will result into the establishment of a new family of human-machine interfaces based on wearable sensors that adapt their functions to their users' abilities.

Studienübersicht

Status

Unbekannt

Detaillierte Beschreibung

The goal of these studies is to enable persons paralyzed by spinal cord injury (SCI) to drive powered wheelchairs and interact with computers by acting through an interface that maximizes the effectiveness of their residual motor function. This is called a "body-machine interface" because it maps the motions of the upper-body (arms and shoulders) to the space of device control signals in an optimal way. In this way, paralyzed persons that cannot operate a joystick controller because of lack of hand mobility can effectively use their whole upper body as virtual joystick device. An important characteristic of the proposed approach is that it is based on the possibility to control a computer or a wheelchair by bodily movements through an interactive learning process, in which the interface adapts itself to the subject's mobility and the subject learns to act through the interface. This study aims at developing and testing the customization of this interface to a group of SCI participants with tetraplegia, resulting from high-level cervical injury. The proposed research is organized in three specific aims:

(Aim 1) To develop new functional capabilities in persons with spinal cord injury by customizing a body-machine interface to their individual upper body mobility. After fitting the interface to the residual movements of each subject, participants will practice computer games aimed at training two classes of control actions: operating a virtual joystick and operating a virtual keyboard. This study will test the ability of the subjects to perform skilled maneuvers with a simulated wheelchair.

(Aim 2.) To test the hypothesis that practicing the upper-body control of personalized interfaces results in significant physical and psychological benefits after spinal-cord injury. A study will evaluate and quantify the impact of the practicing functional upper-body motions on the mobility of the shoulder and arms by conventional clinical methods and by measuring the subjects' ability to generate coordinated upper body movements and to apply isometric forces. Other studies under this aim will evaluate the effects of operating the body-machine interface on musculoskeletal pain and on the mood and mental state of the participants.

(Aim 3) To train spinal-cord injury survivors to skillfully operate a powered wheelchair using their enhanced upper body motor skills and customized interface parameters. Finally, the last study will test the hypothesis that the skills learned through practice in the virtual environment are retained for the control of an actual powered wheelchair. After reaching stable performance in the simulated wheelchair, subjects will practice the control of the physical wheelchair in safe a testing environment.

(Aim 4.) To understand how extensive practice with a body machine interface affects the cortical representation of the trained limbs. A study will evaluate and quantify the impact of the practicing functional upper-body motions on corticospinal excitability as a correlate to sensorimotor skill learning. Participants will meet the inclusion criteria for both the main study and satisfy the additional optional criteria. Participant will practice upper-body movements using the body-machine interface. The study will evaluate the evolution of corticospinal excitability in related areas of the motor cortex during the training compared to the baseline and after a follow-up period.

If successful, this study will lead to effective operation of a highly customized interface that adapts to the residual motor capability of its users. Physical and psychological benefits are expected to derive from the sustained and coordinated activity associated with the use of this body-machine interface

Studientyp

Interventionell

Einschreibung (Tatsächlich)

157

Phase

  • Unzutreffend

Kontakte und Standorte

Dieser Abschnitt enthält die Kontaktdaten derjenigen, die die Studie durchführen, und Informationen darüber, wo diese Studie durchgeführt wird.

Studienorte

    • Illinois
      • Chicago, Illinois, Vereinigte Staaten, 60611
        • Shirley Ryan AbilityLab

Teilnahmekriterien

Forscher suchen nach Personen, die einer bestimmten Beschreibung entsprechen, die als Auswahlkriterien bezeichnet werden. Einige Beispiele für diese Kriterien sind der allgemeine Gesundheitszustand einer Person oder frühere Behandlungen.

Zulassungskriterien

Studienberechtigtes Alter

18 Jahre bis 65 Jahre (Erwachsene, Älterer Erwachsener)

Akzeptiert gesunde Freiwillige

Nein

Studienberechtigte Geschlechter

Alle

Beschreibung

Inclusion Criteria:

  • Age 18-65
  • Injuries at C3-C6 level, complete (ASIA A) or incomplete (ASIA B and C)
  • Able to follow simple commands
  • Able to speak or respond to questions

Exclusion Criteria:

  • Presence of tremors, spasm and other significant involuntary movements
  • Cognitive impairment
  • Deficit of visuo-spatial orientation
  • Concurrent pressure sores or urinary tract infection

(Optional) Additional Exclusion Criteria for evaluation of corticospinal excitability using Transcranial Magnetic Stimulation:

  • Any metal in head with the exception of dental work or any ferromagnetic metal elsewhere in the body. This applies to all metallic hardware such as cochlear implants, or an Internal Pulse Generator or medication pumps, implanted brain electrodes, and peacemaker.
  • Personal history of epilepsy (untreated with one or a few past episodes), or treated patients
  • Vascular, traumatic, tumoral, infectious, or metabolic lesion of the brain, even without history of seizure, and without anticonvulsant medication
  • Administration of drugs that potentially lower seizure threshold [62], without concomitant administration of anticonvulsant drugs which potentially protect against seizures occurrence
  • Change in dosage for neuro-active medications (Baclophen, Lyrica, Celebrex, Cymbalta, Gapapentin, Naposyn, Diclofenac, Diazapam, Tramadol, etc) within 2 weeks of any study visit.
  • Skull fractures, skull deficits or concussion within the last 6 months
  • unexplained recurring headaches
  • Sleep deprivation, alcoholism
  • Claustrophobia precluding MRI
  • Pregnancy

Studienplan

Dieser Abschnitt enthält Einzelheiten zum Studienplan, einschließlich des Studiendesigns und der Messung der Studieninhalte.

Wie ist die Studie aufgebaut?

Designdetails

  • Hauptzweck: Unterstützende Pflege
  • Zuteilung: Nicht randomisiert
  • Interventionsmodell: Parallele Zuordnung
  • Maskierung: Single

Waffen und Interventionen

Teilnehmergruppe / Arm
Intervention / Behandlung
Experimental: SCI Static
SCI group that practices with a static body-machine map
The intervention compares two ways of customizing the body-machine interface which will be used for subjects for 40 sessions (spread over 8 months). In one case (SCI static), the body-machine interface is static. In the other case (SCI Machine Learning), there is a machine learning algorithm that adapts to the movements made by the subject.
Experimental: SCI Machine Learning
Spinal Cord Injury patients who practice with a body-machine map that is adapted using machine learning
The intervention compares two ways of customizing the body-machine interface which will be used for subjects for 40 sessions (spread over 8 months). In one case (SCI static), the body-machine interface is static. In the other case (SCI Machine Learning), there is a machine learning algorithm that adapts to the movements made by the subject.

Was misst die Studie?

Primäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Change in Time to task completion from Baseline at 8 months
Zeitfenster: Baseline and 8 months
The subjects will perform computer games requiring different data entry tasks (characters, cursor control) and navigate either a virtual or a real obstacle course. This primary outcome measure is the time it takes subjects to complete each task.
Baseline and 8 months

Sekundäre Ergebnismessungen

Ergebnis Maßnahme
Maßnahmenbeschreibung
Zeitfenster
Change in Movement Smoothness from Baseline at 8 months
Zeitfenster: Baseline and 8 months
This outcome measure measures the change in movement smoothness when operating the virtual and real wheelchairs
Baseline and 8 months
Change in Strength
Zeitfenster: Baseline and 8 months
This outcome measure measures the changes in upper body strength after training
Baseline and 8 months
Change in Mental State
Zeitfenster: Baseline and 8 months
This outcome measures measures the change in mental state (as quantified by the State-Trait Anxiety Inventory) after training
Baseline and 8 months

Mitarbeiter und Ermittler

Hier finden Sie Personen und Organisationen, die an dieser Studie beteiligt sind.

Ermittler

  • Hauptermittler: Ferdinando A Mussa-Ivaldi, PhD, Northwestern University

Publikationen und hilfreiche Links

Die Bereitstellung dieser Publikationen erfolgt freiwillig durch die für die Eingabe von Informationen über die Studie verantwortliche Person. Diese können sich auf alles beziehen, was mit dem Studium zu tun hat.

Studienaufzeichnungsdaten

Diese Daten verfolgen den Fortschritt der Übermittlung von Studienaufzeichnungen und zusammenfassenden Ergebnissen an ClinicalTrials.gov. Studienaufzeichnungen und gemeldete Ergebnisse werden von der National Library of Medicine (NLM) überprüft, um sicherzustellen, dass sie bestimmten Qualitätskontrollstandards entsprechen, bevor sie auf der öffentlichen Website veröffentlicht werden.

Haupttermine studieren

Studienbeginn

1. Februar 2013

Primärer Abschluss (Voraussichtlich)

1. September 2022

Studienabschluss (Voraussichtlich)

1. September 2022

Studienanmeldedaten

Zuerst eingereicht

16. April 2012

Zuerst eingereicht, das die QC-Kriterien erfüllt hat

28. Mai 2012

Zuerst gepostet (Schätzen)

31. Mai 2012

Studienaufzeichnungsaktualisierungen

Letztes Update gepostet (Tatsächlich)

15. November 2019

Letztes eingereichtes Update, das die QC-Kriterien erfüllt

14. November 2019

Zuletzt verifiziert

1. November 2019

Mehr Informationen

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