Motor Learning in a Customized Body-Machine Interface

NCT ID: NCT01608438

Last Updated: 2019-11-15

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 157 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2013-02-28
• Study Completion Date: 2022-09-30

Brief 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.

Detailed Description

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

Conditions

Spinal Cord Injury

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: SINGLE

Study Groups

SCI Static

SCI group that practices with a static body-machine map

Group Type: EXPERIMENTAL

Customizing the Body-Machine Interface

Intervention Type: DEVICE

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.

SCI Machine Learning

Spinal Cord Injury patients who practice with a body-machine map that is adapted using machine learning

Group Type: EXPERIMENTAL

Customizing the Body-Machine Interface

Intervention Type: DEVICE

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.

Interventions

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.

Intervention Type: DEVICE

Eligibility Criteria

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

• 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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

Shirley Ryan AbilityLab

OTHER

Sponsor Role: lead

Responsible Party

Ferdinando Mussa-Ivaldi

Senior Research Scientist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Ferdinando A Mussa-Ivaldi, PhD

Role: PRINCIPAL_INVESTIGATOR

Northwestern University

Locations

Shirley Ryan AbilityLab

Chicago, Illinois, United States

Countries

United States

References

Abdollahi F, Farshchiansadegh A, Pierella C, Seanez-Gonzalez I, Thorp E, Lee MH, Ranganathan R, Pedersen J, Chen D, Roth E, Casadio M, Mussa-Ivaldi F. Body-Machine Interface Enables People With Cervical Spinal Cord Injury to Control Devices With Available Body Movements: Proof of Concept. Neurorehabil Neural Repair. 2017 May;31(5):487-493. doi: 10.1177/1545968317693111. Epub 2017 Feb 1.

Reference Type: BACKGROUND

PMID: 28413945 (View on PubMed)

De Santis D, Mussa-Ivaldi FA. Guiding functional reorganization of motor redundancy using a body-machine interface. J Neuroeng Rehabil. 2020 May 11;17(1):61. doi: 10.1186/s12984-020-00681-7.

Reference Type: DERIVED

PMID: 32393288 (View on PubMed)

Other Identifiers

STU00057856

Identifier Type: -

Identifier Source: org_study_id