video demo https://www.youtube.com/watch?v=yRegP_kmsGA #Inspiration The inspiration was to utilize the algorithms being developed for Machine Learning in combination with recent advancements in the field of experimental neuroscience. The goal was to create a prototype product that has tremendous potential in the intersection of software, hardware and neuroscience.
#What it does Drowsy Alert is an Android application that processes raw EEG data obtained from the Muse headband in order to create a warning system for drowsy drivers on the road. The application processes the brain waves of a driver on the highway and takes preventative measures towards stopping the occurrence of an accident. Such preventative measures include, but are not limited to, alerting the driver through voice, alarm systems, and ultimately reaching out to the driver's emergency contacts.
#How I built it We built the application through a combination of the Android SDK, the Muse API, Google Text-to-Speech API, Android Material Design UI principles and a plethora of native Android APIs such as geolocation, SMS etc.
#Challenges I ran into We ran into challenges primarily in the processing of raw EEG signals into understandable, accurate data.
#Accomplishments that I'm proud of We are proud of how much we have learned over this weekend, and how much new information in the relevant fields we have been exposed to.
#What I learned We learnt quite a bit about Machine Learning algorithms, Digital Signal Processing and Android background processes.
#What's next for Drowsy-Alert Our goal is to continuously improve upon our prototype product with better signal processing, improved algorithms for alert systems, and better prediction of accidents on the road.