Cognitive-based Prosthetics to Improve Grasp and Reaching After SCI
Condition: Spinal Cord Injuries; Hand Weakness
MeSH terms: Spinal Cord Injuries
Study type: Interventional

Summary: 
      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: 
      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.
    

Intervention name(s): Cognition glove; Sensory brace
Intervention type(s): Device; Device
Intervention description(s): 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).; 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
Allocation: Randomized
Intervention model: Crossover Assignment
Intervention 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 (Outcomes Assessor)
Primary purpose: Device Feasibility