Mendi is a brain health system designed for home use. The system consists of three key components: a headset to measure brain activity based on neurofeedback technology, a ‘brain training’ mobile app to help control and enhance brain function in the prefrontal cortex , and intelligent lifestyle content to improve brain health more broadly. For the first time, powerful neurotechnology can be used to take care of your brain health right at home.
Using Swift and Kotlin, we created an advanced application capable of exporting data from a database and displaying graphs to summarise your personal neurological data
Using React, we created an advanced web application capable of displaying graphs to summarise your personal neurological data
In order to provide recommendations for how to improve your life, we researched suitable machine learning algorithms that can be used
Sprint 1 was dedicated to researching different companies and how they perform export features and how to display said data in the form of graphs, pie charts and etc. In addition, Team Gluon had to get to a basic understanding on the programming concepts required. Namely APIs, Databases, Front End Languages. It was decided that React would be used in order to create a responsive website, Kotlin will be used to create an Android app and Swift would be used to create an IOS app.
Sprint two was dedicated to creating a responsive but static app and learning how to create a responsive but static website. Static refers to a lack of connection with a backend API and responsive refers to working properly on devices with different screen sizes.
Sprint 3 was dedicated to working on creating and adding the export feature for Android and iOS. The user could press a button in the UI to generate an email with an attached .csv-file of the data that would be sent to the user's associated email. We set up Google Firestore to host our data and access it by authenticating the users with their email addresses and password. We also got to know some technical parts of creating graphs to visualize the data inside the UI:s. We also managed to create a website to host some graphs used by users that would enter through the Mendi website instead of the apps.
Sprint four was dedicated to writing documentation about how the export and visualization features work in Android and iOS apps as well as having documentation of potential machine learning algorithms that can be used by Mendi. Also, we created the website for displaying data from the local database using graphs and charts.
Email: gluonteam@gmail.com
Open: 20th and 21st May
10:00 - 11:00
15:00 - 16:00
Zoom Link: https://kth-se.zoom.us/j/67056453054
Team Members: Alex, Edward, Filip, Amir, Hasti, Kocin, Johan