Inspiration

Our grandparents are aging and are losing their natural reflexes. Recently, a close family friend was out walking her dog when she fell and broke her hip and got a concussion. Unfortunately, there were no bystanders on the scene to help out and she was in pain for over an hour before the paramedics arrived. Elderly people require much more attention and care, and often, human care givers cannot provide that support. Therefore, we hacked easily accessible hardware to provide real-time monitoring and alerts of the elderly patient's well being.

What it does

Our application leverages 4 main components:

  1. The Turtlebot3 uses LIDAR to track the elderly. When a fall is detected, the Turtlebot3 surveys the situation and projects the live stream to our WebApp. The Turtlebot also calls our own API with medical information. Turtlebot3 Demo
  2. The Fitbit Iconic and its API are used to monitor the individual's heart rate, acceleration and GPS location. Any anomalies mapping to a fall will result in calls to our API warning our caregiver of the incident via SMS. Live Stream from Turtlebot3
  3. Our custom API leverages the Firebase Realtime Database to store warnings, messages, images, and videos of the patient from the Turtlebot3. It also acts as a middleware between the Turtlebot3 and Twilio (to text caregivers). Our API POSTS this information on a web application (4). Alerts via call and SMS
  4. Our web application which hosts all of the information retrieved from the Turtlebot3 leveraging Flask and Python.

How we built it

Languages: C++, Python, Javascript Frameworks/Other: Flask, Node.js, Firebase, SQL, REST API, AJAX

Challenges we ran into

Limited support and bandwidth for our specific IoT devices. Hardware issues with Turtlebot3's various sensors. Firewall and network security issues in communications. Virtualization of various OS.

Accomplishments that we're proud of

We all learned something new this weekend. We all made meaningful contributions to the project -- Team work makes the dream work!

What we learned

Install VMs ahead of time. Don't give up on the code and BELIEVE.

What's next for Alertify

Better detection of falls, leveraging machine learning. Sleep for the team members.

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