Motor Learning in a Customized Body-Machine Interface (BMI)
Motor Learning in a Customized Body-Machine Interface for Persons With Paralysis
研究概览
详细说明
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
研究类型
注册 (实际的)
阶段
- 不适用
联系人和位置
学习地点
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Illinois
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Chicago、Illinois、美国、60611
- Shirley Ryan Abilitylab
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参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
描述
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
学习计划
研究是如何设计的?
设计细节
- 主要用途:支持治疗
- 分配:非随机化
- 介入模型:并行分配
- 屏蔽:单身的
武器和干预
参与者组/臂 |
干预/治疗 |
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实验性的:SCI Static
SCI group that practices with a static body-machine map
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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.
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实验性的:SCI Machine Learning
Spinal Cord Injury patients who practice with a body-machine map that is adapted using machine learning
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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.
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研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Change in Time to task completion from Baseline at 8 months
大体时间:Baseline and 8 months
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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.
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Baseline and 8 months
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Change in Movement Smoothness from Baseline at 8 months
大体时间:Baseline and 8 months
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This outcome measure measures the change in movement smoothness when operating the virtual and real wheelchairs
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Baseline and 8 months
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Change in Strength
大体时间:Baseline and 8 months
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This outcome measure measures the changes in upper body strength after training
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Baseline and 8 months
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Change in Mental State
大体时间:Baseline and 8 months
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This outcome measures measures the change in mental state (as quantified by the State-Trait Anxiety Inventory) after training
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Baseline and 8 months
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合作者和调查者
调查人员
- 首席研究员:Ferdinando A Mussa-Ivaldi, PhD、Northwestern University
出版物和有用的链接
一般刊物
- 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.
- 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.
研究记录日期
研究主要日期
学习开始
初级完成 (预期的)
研究完成 (预期的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (估计)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- STU00057856
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