OmniBliss

Introduction:

Continuing our legacy from OmniEther (Hack 36 3.0), we present to you all this year's project, OmniBliss.
Presentation Link - https://drive.google.com/file/d/1qg6ScsP_o1TiJP33aHn7aX6UACl-a4Bi/view?usp=sharing
Youtube Video Link - https://www.youtube.com/watch?v=rtIGD-64NwI

Table of Contents:

1. Technology Stack
2. Survey
3. Problem Statement
4. Proposed Solution
5. Implementation Details
6. Wow Factor
7. Future Work
8. Contributors

Technology Stack:

• Hardware Realted - MI Band Series and Bluetooth 5.0
• Backend Related - Django Web Framework and Django Rest Framework
• Frontend Related - Flutter and Dart
• Machine Learning Related - Numpy, Pandas, Scikit-Learn, Matplotlib and Seaborn
• Collaboration Related - Git and Github

Survey (By kff.org)

• During the pandemic, about 4 in 10 adults in the U.S. have reported symptoms of anxiety or depressive disorder.
• This number increased from one in ten adults who reported these symptoms from January to June 2019.
• Similar is the case with citizens of other countries as well.

Problem Statement

• Devising a way to tackle these increasing levels of stress and anxiety among the general population and create an environment of Bliss.

Proposed Solution

• Assigning user to a cluster (in backend) based on profile data provided.
• Connecting user’s wearable device (MI Band) to our app using Bluetooth.
• Collecting user’s real-time Data (Heart Beat, Steps, etc) from wearable devices (MI Band).
• Calculating Heart Rate Variability from that data (R-R intervals).
• Detecting whether the user is stressed or not from above data using a pre-trained ML model.
• Recommending user some activities to reduce the stress level based on the cluster user belongs to.

Implementation Details

• We are recording 100 consecutive R-R intervals from the wearable device.
• We send this value to the Server for further calculation.
• 18 HRV parameters are calculated based on this value.
• Time domain features - Mean_RR, SDNN, SDSD, RMSSD, CVSD, etc. Frequency domain features - LF, HF, TP, LF/HF Ratio, etc.
• Using this data, we predict whether user is stressed or not.
• If the user is stressed, we recommend user some activities based on pre - determined cluster.

Wow Factor

• Emergency Contact (SOS) Button.
• Relaxing UI.
• Soothing Background Music in the app.

Future Work

• Generalising our app for most of the wearable devices in market like Fitbits, MI Band(s), AmazFit, etc.
• Creating our own hardware.
• Improving Recommendation System.

Contributors:

Team Name: EnigmaHaxx

• Ankit Sangwan
• Aritra Chatterjee
• Manish Kumar
• Kshitiz Srivastava

Made at: