Cognitive-based Prosthetics to Improve Grasp and Reaching After SCI

Study Overview
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Brief Summary
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Rehabilitation of functional movements after spinal cord injury (SCI) requires commitment and engagement to the processes of physical therapy. Outcomes may be improved by techniques that strengthen cognitive connections between users and physical therapy exercises. The investigators will investigate combinations of virtual reality and innovative wearable technology to accelerate rehabilitation of hand grasp and reach. These devices use multi-sensory feedback to enhance the sense of agency, or feelings of control, and better train movements during physical rehabilitation exercises. The investigators will measure the effect of these devices on improving the speed, efficiency, and accuracy of performed movements in Veterans with SCI.

Detailed Description
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Spinal cord injury (SCI) at the cervical level impairs hand function severely compromises performance of activities of daily living. The physical rehabilitation process requires commitment by the participant to achieve meaningful gains in function. Rehabilitation approaches that are cognitively engaging can facilitate greater commitment to practice and improved movement learning. The investigators propose to develop innovative platforms that utilize virtual reality (VR) and instrumented wearables that enhance cognitive factors during motor learning of hand grasp and reach after SCI. These factors include greater sense of agency, or perception of control, and multi-sensory feedback. Sense of agency is implicated with greater movement control, and various sensory feedback modalities (visual, audio, and haptic) are proven effective in movement training. However, these factors are not well considered in traditional physical therapy approaches. The investigators have developed two novel cognitive-based platforms for rehabilitating grasp and reach function and propose to test each platform in Veterans with chronic SCI at the cervical level. Aim 1 will investigate how the cognition glove may improve functional grasp. This glove includes force and flex sensors that provide inputs to a machine learning algorithm trained to predict when secure grasp is achieved. The glove alerts the user of secure grasp through onboard sensory modules providing visual (LED), audio (beeper), and tactile (vibrator) feedback. During training, feedback is provided at gradually shorter time-intervals to progressively induce agency based on the neuroscience principle of 'intentional binding'. This principle suggests that with greater agency, one perceives their action (i.e., secure grasp) is more coupled in time to a sensory consequence (i.e., glove feedback). The glove is user-ready, and now has compatibility with customized VR applications to provide enhanced sensory feedback through engaging and customized visual and sound alerts. The investigators hypothesize that enhanced feedback in VR will produce even greater improvements in grasp performance than onboard feedback alone. Aim 2 will investigate how Veterans with SCI may learn greater arm muscle control during virtual reaching while using a sensory brace that provides isometric resistance to one arm to elicit electromyography (EMG) patterns that can drive a virtual arm. The person receives visual feedback from VR and muscle tendon haptic feedback from the brace during training. Tendon stimulation can elicit movement sensations that modulate muscle activation patterns. The VR feedback will provide conscious movement training cues while vibration feedback will subconsciously elicit more distinct EMG patterns based on cluster analysis. The investigators hypothesize that the promotion of distinct EMG patterns, achieved by maximizing inter-cluster distances, will improve performance of a reach-to-touch task. Importantly, the concept of strengthening cognitive agency and learning of movement using wearable technology, multi-sensory feedback, and virtual reality during physical training will be applicable to all forms of neuromuscular impairment, including stroke and traumatic brain injury in addition to SCI.

Official Title
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Cognitive-based Rehabilitation Platform of Hand Grasp After Spinal Cord Injury Using Virtual Reality and Instrumented Wearables

Conditions
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Hand Weakness, Spinal Cord Injuries

Intervention / Treatment
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* Device: Cognition glove
* Device: Sensory brace


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: SCI occurred greater than 12 months ago SCI occurred between levels C1-T1 Hand weakness: score of 2, 3, or 4 out of 5 on manual muscle testing of finger extension, finger flexion, or finger abduction in either hand Exclusion Criteria: History of other serious brain or spinal cord injuries History of seizures Ventilator dependence; open tracheostomy Use of medications that significantly lower seizure threshold History of significant cognitive deficits Open skin lesions over the face, neck, shoulders, arms, or hands Pregnancy

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 65 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Device Feasibility
Allocation: Randomized
Intervention Model: Crossover Assignment
Interventional Model Description: At each visit, prototype cognitive prosthetic devices will be tested under 3 conditions: No feedback, simple feedback, and enhanced feedback. Outcomes will include time taken for successful movements; and accuracy of movements.
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: No cognitive feedback<br>Perform task without cognitive feedback. | Device: Cognition glove<br>* The investigators have developed and tested a functional prototype of an instrumented glove to alert the user about secure grasp of objects. Onboard force and flex sensors provide inputs to a machine learning algorithm (artificial neural network, ANN) to estimate secure grasp based on previously collected training data. The glove enhances agency by alerting the user to secure grasp through sensory feedback modules (visual - LED, audio - beeper, tactile - vibrator).<br>Device: Sensory brace<br>* A a size- and position-adjustable arm brace with weight-support capability and housing for vibration motors and EMG sensors. Position adjustment allows for physical therapists to find and recommend arm postures that are clinically relevant to each person. The participant can then isometrically push/resist against the brace to strengthen target muscles while performing VR reach-to-touch. The person will receive visual feedback from the virtual environment to train movement performance and vibrotactile feedback at tendons to subconsciously adjust their muscle activation patterns<br>|
| Active Comparator: Intermediate feedback.<br>Perform task with intermediate feedback. | Device: Cognition glove<br>* The investigators have developed and tested a functional prototype of an instrumented glove to alert the user about secure grasp of objects. Onboard force and flex sensors provide inputs to a machine learning algorithm (artificial neural network, ANN) to estimate secure grasp based on previously collected training data. The glove enhances agency by alerting the user to secure grasp through sensory feedback modules (visual - LED, audio - beeper, tactile - vibrator).<br>Device: Sensory brace<br>* A a size- and position-adjustable arm brace with weight-support capability and housing for vibration motors and EMG sensors. Position adjustment allows for physical therapists to find and recommend arm postures that are clinically relevant to each person. The participant can then isometrically push/resist against the brace to strengthen target muscles while performing VR reach-to-touch. The person will receive visual feedback from the virtual environment to train movement performance and vibrotactile feedback at tendons to subconsciously adjust their muscle activation patterns<br>|
| Experimental: Enhanced feedback<br>Perform task with virtual reality and/or haptic feedback. | Device: Cognition glove<br>* The investigators have developed and tested a functional prototype of an instrumented glove to alert the user about secure grasp of objects. Onboard force and flex sensors provide inputs to a machine learning algorithm (artificial neural network, ANN) to estimate secure grasp based on previously collected training data. The glove enhances agency by alerting the user to secure grasp through sensory feedback modules (visual - LED, audio - beeper, tactile - vibrator).<br>Device: Sensory brace<br>* A a size- and position-adjustable arm brace with weight-support capability and housing for vibration motors and EMG sensors. Position adjustment allows for physical therapists to find and recommend arm postures that are clinically relevant to each person. The participant can then isometrically push/resist against the brace to strengthen target muscles while performing VR reach-to-touch. The person will receive visual feedback from the virtual environment to train movement performance and vibrotactile feedback at tendons to subconsciously adjust their muscle activation patterns<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Time to achieve secure grasp upon initial contact | Time to achieve secure grasp. Lower time is better. | Iimmediately after the procuedure | 
| Time to complete pick-up and placement of object | Time to pick up and re-place object. Lower time is better. | Iimmediately after the procuedure | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Motion pathlength in moving object | Pathlength during reaching/grasping/moving. Shorter is better. | Iimmediately after the procuedure | 
| Error in placing object onto target | Errors during reaching/grasping/moving. Fewer is better. | Iimmediately after the procuedure |