Inspiration
We were first introduced to the idea of helping patients with their rehabilitation by a friend in neuroscience. We were immediately drawn to the idea of creating a solution that would help people regain their ability to grasp objects using their hands. It would assist them in training their fingers and hands to be able to move again. It would efficiently combine all of our skillsets in a really exciting prospect.
What it does
It is intended to help patients with the rehabilitation of motor skills in their hands by providing "self-assisted" electrical stimulation to the area. It is self-assisted because the level of stimulation is determined their level of focus on the intention of grasping.
How we built it
We created a sketch that included all the necessary commands to control our LEDS and level of stimulation for the TENs pads then uploaded it to the arduino. We then we created a python script to connect to Muse 2 , process its EEG signals from the 4 channels and then convert that to appropriate levels of stimulation in the TENs pads. The levels of stimulation also corresponded to different colours in LED lights to help patients with limited sense in their hands to get feedback on how hard they are grasping.
Challenges we ran into
Some minor challenges we overcame were figuring our the right settings for the hardware and its compatibilty with certain dev environments. The biggest challenge we were not able to overcome in the given time span was connecting another auxliary sensor to the muse which would have enabled use to get more accurate EEG readings that correspond to the grasping motion.
Accomplishments that we're proud of
We managed to develop a working end to end prototype. So we collected data from the Muse 2 then used a NeuroStim Arduino to process those signals and create commands for the TENS patches.
What we learned
We learnt a lot about all the hardware we tried. Since all of our team came from a strict programming background, the entire for days was a very fast-paced and high-energy learning process,
What's next for B.A.R.B.I.E
Portability By making the device portable in size, we address one of the biggest challenges in rehabilitation: adherence to therapy. When the device is user-friendly and accessible outside clinical settings, users are more likely to stay engaged with their therapy plan.
User-Friendliness We look to integrate our current GUI onto the system that allows the user to customize the range of strength of the simulations.
Accuracy With more time, resources, and funding we would be able to collect more data, as well as target brain-waves more specific to the motor functions.
Multi-Device and App Integration We aim to integrate our product with people’s smartphones, smartwatches, and health apps with the goal to improve an individual’s general health to further improve our ability to rehabilitate those in need.
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