Input to Machine Learning Algorithm
Nearly half of all African-American adults have some form of cardiovascular disease,48 percent of women and 46 percent of men. Heart disease is the No. 1 cause of death in the world and the leading cause of death in the United States, killing over 375,000 Americans a year.We inspired by analysing the irregularities with rate of the heartbeat and make everyone feel cautious about themselves. Self safety is something that everyone should practice and we provide with a platform that provides protection by analysing the surges in one of the most important organs present in our body.
What it does
LIFE.Tech is a platform for people who suffer from heart rate turbulence and need immediate assistance as a matter of relating to failures of any organ in the body. Our goal is to create a system that monitors a person’s heart rate throughout the day and pushes it to our system where we analyse whether the organ is behaving normally or not. Our sole purpose is to target those audiences who suffer from panic attacks, heart attacks or who are under the influence of heavy dosage of drugs hence providing them with a service what are the next steps they should take in order to get to safety and also alert the emergency contacts of these people. We also provide immediate health assistance to those who are in severe need, while generating a detailed report of what caused the symptoms to trigger to and submitting them to the user’s family and doctor.
How we built it
The ESP 8266 is reading the data from the heartbeat sensor and is connected to wifi. It then pushes this data to the Amazon Web Service which has a RDS instance up and running with a MySql already setup on it. We store the data on this mysql database and we use flask to publish this data on the server that runs an ubuntu image. The data is picked up by the machine learning algorithm(Artificial Neural Networks) where the network is being trained to determine the bad heartbeats with the good one’s in order to increase the efficiency of the system. The data is picked up in a JSON format where the heart rate monitor UI will show user’s heartbeat being generated in real time. We have a software trigger that will tell us whether the user is suffering from a heart attack and using Twilio application, it will send SMS to user’s family and doctor for urgent care.User will also get one voice message which consists details of what are the precautions user can take for heart attack before any emergency care arrives.
Challenges we ran into
The main challenge we faced was getting the data from aws service using flask and training the data using machine learning especially with Wolfram Language and Artificial Neural Networks.
Accomplishments that we're proud of
Staying awake without caffeine for the weekend and submitting a hack to devpost.
What we learned
It is not impossible to create an application that reads a user’s heartbeat and filters out data based on machine learning. It takes a great deal of effort and patience to come together and build a system. A team can generate productivity through teamwork, hardwork and determination.
What's next for lifetech
GPRS system for users Machine learning code on the local machine.