Here in the Neuroengineering and Rehabilitation Laboratory at West Virginia University, we study functional mechanisms of sensory motor control and develop objective assessment tools for the assessment of motor skills and movement impairment. Remote or virtual care has become a promising means to address healthcare disparities. However, there's a critical need to quantitatively characterize motor skills for use in these applications.
Our protocol seeks to enable clinicians and researchers to obtain high-resolution data on complex movements in order to better assess movement deficits remotely. This protocol aims to simplify complex and underused rehabilitation technologies by automating at-home movement assessment. Its key advantage lies in integrating virtual reality, motion capture, and electromyography.
For example, a VR headset with hand tracking captures movements for first analysis, while an EMG bracelet estimate muscle performance during virtual testing. To begin, obtain all the system components required for the motion and muscle activity test. Turn on the electromyography's base station and connect it to a dedicated computer.
To set up the motion capture server, first connect it to a computer link network router, then attach it to a monitor and cameras for visualization. Switch on the third dedicated computer connected to the virtual reality headset, and load the intended task environment into it. Using a custom software function, synchronize the systems in time.
To prepare the testing area, remove all the obstacles, and position an armless sturdy chair at the center. To perform electromyography or EMG, first, set up all the system components. Clean each sensor with an alcohol wipe, and prepare for use with double-sided adhesive tape.
Prior to sensor placement, clean the area with an alcohol wipe, and palpate the muscle belly on the arm as the subject contracts the relevant muscle. Then place the EMG sensors parallel to the direction of the muscle fibers. To calibrate the motion-tracking cameras, move the wand throughout the experimental space and set the 3D axis of the space.
Place the LED motion capture markers on the bony landmarks of the subject's upper extremity and trunk. Using the motion capture software, confirm the recognition of all the markers by the cameras. After calibrating the VR headset, instruct the subject to sit on the chair and place the VR headset on their head.
Measure the length of the subject's arm, followed by the distance between the subject's shoulder and the ground. In the VR task control script, set the side of the virtual block spawning to left or right, the desired number of block spawn repetitions, and the subject measurements. Next, provide a brief explanation of the task to the subject.
Start the task in virtual reality. Instruct the subject to pick up the virtual block, transport it over the partition, and place it onto the target in the opposing compartment. Then start collecting the EMG in motion capture data.
Instruct the subject to begin the VR skill assessment task. The task will end automatically. After that, stop the EMG data collection, and the data will be saved automatically for post-hoc analysis.
EMG profiles of a healthy subject performing the task showed high muscle activation of the anterior deltoid, which is the primary mover of the arm. The forearm and wrist extensors were also notably activated to support grasping. Finally, increased activity of thumb muscles indicated their use in the grasp and release of the block.
