A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

The overall goal of this procedure is to apply video game sensor technology towards a low cost human machine interface for balance rehabilitation. This is accomplished by first setting up mobile brain body imaging, or MoBI equipment, and software. The second step is to place MoBI sensors on the body to capture physiological data.

Next, an eye tracker is used to evaluate post stroke visual pursuit eye movements. Eye tracking will show that gaze related indices can help in delineating post-stroke oculomotor deficits, which may be relevant for planning visuomotor balance therapy. Then, sensors for neuromuscular electrical stimulation, or NMES, assisted in visuomotor balance therapy, under MoBI, are calibrated.

The final step is to collect multi-sensor data from low cost sensors during visuomotor balance therapy. We are extending this technology for our home base rehabilitation for stroke patients who suffer from balance loss. Our system includes gaze based, prognostic dual, and one gaze spectrum as an important ingredient in visuomotor balance therapy.

So this paradigm shifting technology captures how the visual system, the motor system, and the balance systems walks together after stroke and tries to rehabilitate them through training. So in principle, this technic can be used in other conditions, like Autism Spectrum Disorder, Parkinsonism, Traumatic Brain Injury, and also psychomotor disturbance due to depression. All can be rehabilitated in this training system.

Begin by obtaining a projection screen to display the visual bio feedback. Adjust the height so that the center of the screen will be at the subject's eye level. Then place the motion caption sensor in front of the projection screen and aim it at the volume of motion capture.

Confirm that the volume of motion capture is 1.5 meters to 2.5 meters in front of the motion capture sensor. Next, place the balance board on the floor, about 2.0 meters away from the motion capture sensor. Leave enough room around the balance board to ensure full body movement.

To begin sensor placement for electromyography, or EMG, in the NMES system, ask the subject to sit on a chair. Place the EMG and NMES electrodes bilaterally on the Medial Gastrocnemius and Tibialis Anterior muscles of the subject. Then, connect the electrodes to the wireless NMES simulator system.

Next, place the electroencephalogram, or EEG cap, on the subject's head by following the international 10-20 system. Use conductive paste to adhere the electrodes, seen here to the scalp, before connecting them to the wireless EEG headset. Place two passive EEG electrodes above and below one eye to capture the vertical electrooculogram, or EOG.

Add another two to the outer campus of each eye to capture the horizontal EOG. Finally, place two passive EEG electrodes on the subject's earlobes to serve as EEG reference electrodes. Begin by placing the eye tracker just below the visual feedback computer monitor and connect it to the visual feedback computer using a USB 3 port.

Then, ask the subject to sit at the desk at a distance of 50 centimeters from the eye tracker, adjust the visual feedback computer monitor so that the subject's eyes are at the center of the monitor. In the visual feedback computer, open the eye tribe server and eye tribe when-you-eye to ensure whether the subject's eyes are detected by the Gooey and to perform the eye tracker calibration routine subsequently. Next, ask the subject to look straight at the visual feedback computer monitor for visual cues.

Run the eye tracker calibration routine by clicking on the calibrate tab in the Gooey. Run the visual underscore stimulus dot exc in the smart eye folder to execute the virtual reality based interface. Subsequently, run the smart eye dot exc program present in the smart eye folder to acquire the subject's eye gaze data that is synchronized with the virtual reality based task.

Ask the subject to follow the fixed and moving visual stimuli at various positions on the visual feedback computer screen in order to evaluate post stroke pursuit eye movements. First, connect the eye tracker and balance board sensors to the visual feedback computer. Turn on the balance board, or BB sensor.

Press the button on the BB sensor to make the remote discoverable in the menu. Click on the show or hide icon in the system's task bar, and click bluetooth device icon. Then, click on the added device option and pair the BB as a bluetooth device without using the code to the visual feedback computer.

Once the balance board is connected to the visual feedback computer, open the VBT folder, and run the We BB interface dot m file to establish Matt lab BB interface. Have the subject stand on the balance board. Click OK to begin the calibration, then enter the subject's weight and click OK again.

Next, connect and turn on the motion capture sensor to the data acquisition computer and ensure that it has fully booted, which is indicated by the green light. Open the LSL folder and start Mocap software to begin streaming of the motion capture sensor data. Turn on the EEG and EOG data acquisition systems connected to the data acquisition computer.

To do this, first open the LSL folder and open open vibe acquisition server with LSL dot cmd. Finally, select emotive EPOC from the menu for EEG and EOG and configure the module by clicking on driver properties. Then, click on connect and click on play to start the acquisition server.

Begin by asking the subject to stand on the balance board and attach a safety harness. Set a minimum baseline NMES level for upright standing by setting the stimulation frequency at 20 hurtz. Then, increase the pulse width and current level in the NMES software installed in the data acquisition computer until upright standing has been achieved.

For NMES assisted VBT, ask the subject to sit on a chair, facing the motion capture sensor with their feet on the balance board. Run the calib sensors program in the data collect folder in order to collect multi sensor calibration data. Finally, ask the subject to perform self initiated maximal reach movements in different directions that affect center of mass location on the visual feedback.

Begin by running the collect baseline program in the data collect folder to collect baseline resting state, eyes open, multi sensor data. To do this, ask the subject to stand still for two minutes and look straight at the center of mass location on the visual feedback. Then, run the collected VBT program in the data collect folder to collect sensor data during VBT.

Connect the visual feedback computers video output to the projection screen and run the smart IVRT tasks dot exe file in the VBT folder in the visual feedback computer to launch the smart IVRT tasks Gooey. Then, ask the subject to pay attention to the audio and visual prompt on the computer screen and steer the cursor as fast as possible towards randomly presented peripheral targets. Following the Move Phase, ask the subject to hold the cursor at the target location for one second.

Following the peripheral hold phase, ask the subject to return back to the central hold position while the system acquires the subject's two d coordinates of center of pressure and the two d coordinates of the gaze data. The eye gaze features were collected with the eye tracker to quantify a subjects performance during a smooth pursuit task for later comparison with post stroke VBT data. Then, for VBT, the protocol used to modify functional reach task to quantify the subject's ability to volitionally shift their center of pressure position as quickly as possible without losing balance, while cued with visual feedback.

Here, EOG data shows that during VBT, the FD ratio, or ratio of fixation duration on the target, and the fixation duration on the cursor, increased while the normalized mean squared error decreased. While attempting this procedure it is very important to consider the safety and the comfort of the stroke patient. We expect that a virtual reality based balance rehabilitation system will be potent to address balance disorders of stroke patients.

So following this procedure, other additional clinical measures, like the clinical measure of balance and mobility can be far from to comprehensively know how quantitive measures compared to other standard clinical parameters so that we can get a comprehensive picture of functional deficit of a stroke patient and we can comprehensively address this issue in the rehabilitation.