MedTrace Logo

Motor Learning in a Customized Body-Machine Interface

NCT01608438 · Status: UNKNOWN · Phase: NA · Type: INTERVENTIONAL · Enrollment: 157

Last updated 2019-11-15

No results posted yet for this study

Summary

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.

Conditions

  • Spinal Cord Injury

Interventions

DEVICE

Customizing the Body-Machine Interface

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.

Sponsors & Collaborators

  • National Institutes of Health (NIH)

    collaborator NIH

  • Shirley Ryan AbilityLab

    lead OTHER

Principal Investigators

  • Ferdinando A Mussa-Ivaldi, PhD · Northwestern University

Study Design

Allocation
NON_RANDOMIZED
Purpose
SUPPORTIVE_CARE
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2013-02-28
Primary Completion
2022-09-30
Completion
2022-09-30

Countries

  • United States

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT01608438 on ClinicalTrials.gov