What we mainly decided to do in our proposed method is designing an application that at first addresses your symptoms to an M.D by taking an in-app questionnaire, and if you are alarmed being parkinsonian, it precisely tries to detect your symptoms using some wearable gadgets like a smartwatch, this will help people to take the main clinical tests at home, as they get used to doing significant tasks online during Covid-19 pandemic.
In the first layer of the app, 3states are referred to, which is for three different types of individuals. The 1st type, those who wanna check if they are high-risk being parkinsonian, 2nd one those an M.D. guessed they might be a Parkinsonian but wanna investigate more accurately using smart gadgets, and 3rd where the one is a Parkinsonian but wanna check if their disease's stage changed or not.
There are some major challenges in the path through the final product for us. For instance, is using just a smartwatch as a wearable gadget enough for our data accumulation? or should we use more wearable sensors? The other major challenge is for the last state, where we will compare test results with EEG results gathered from the patient. We still couldn't find any specific neuromarkers from EEG that differ their brain work with healthy cases, so as our current project, we are trying to find these neuromarkers. Once we got trustable results, we can compare the application and the Deep learning model used for EEG detection to boost the accuracy of both our model and prototype.
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