Covid-19-chatbot-using-IBM-cloud-and-deep-learning

IBM Watson assistant based chatbot with active covid-19 cases across Indian district, automatic voice response, cough detection, heart-rate detection, emotion recognition

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How it works

Alt text](https://www.youtube.com/watch?v=cTnMJP2GgdY)

CHATBOT-Cough detection and automated response

RNN noise reduction

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build librnnoise & rnnoise_demo with CMake

mkdir build
cd build
cmake ..
make

copy the file "rnnoise_demo" from "/build/bin/" to the main directory

download the weight file from https://drive.google.com/file/d/1BV2OSIuuwg6hx-22Q1ApayeWlf45AhmO/view?usp=sharing and move it the "models" directory

Edit the IBM credentials

  1. Find the Assistant service in your IBM Cloud Dashboard Services.
  2. Click on your Watson Assistant service and then click on Launch Watson Assistant.
  3. Use the left sidebar and click on Assistants. Create an assistant.
  4. Select the Dialog skill card and click Next.
  5. Select the Import skill tab.
  6. Click the Choose JSON File button and choose the data/covid_ai_chatbot_skill.json file in your cloned repo.
  7. Click import
  8. Go back to the Skills page (use the left sidebar).
  9. Look for the created skill.
  10. Click on the three dots in the upper right-hand corner of the card and select View API Details. Copy the skill ID(workspace id). Go to your assitant click on the three dots in the upper right-hand corner and select settings and open API details note down the Assistant id( for android application ), assistant URL Save it for the next step.
  11. Go to IBM functions and create an action with python 3.7
  12. Open function_action.py and copy-paste it in the Code section. Click on Parameters and add Parameter with Parameter name as "link" and Parameter Value as " https://api.covid19india.org/state_district_wise.json ". 13. cp sample.env .env

install anaconda python

conda create -n sound pip python=3.6
conda activate sound
pip install -r requirements.txt

Run

python detail_live.py

Principle

  • CONVOLUTIONAL NEURAL NETWORK(CNN) WITH KERAS USING TENSORFLOW BACKEND

  1. Collected sound data: https://voice.mozilla.org/en/datasets, https://urbansounddataset.weebly.com/urbansound8k.html, https://github.com/hernanmd/COVID-19-train-audio/tree/master/not-covid19-coughs

  2. Used transfer learning on the VGG-16 architecture Pre-trained on YouTube-8M for audio recognition

  3. Save the keras model and used for real-time prediction

CHATBOT - Flask

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python app.py

and go to 127.0.0.1:1880 from your browser

CHATBOT-Node-Red

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follow this tutorial https://developer.ibm.com/tutorials/create-a-voice-enabled-covid-19-chatbot-using-node-red/ and import flows.json from folder "node-red app"

CHATBOT-Android application

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Install app-debug.apk

Emotion recognition

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cd Emotion recognition
python main_script.py

Principle

  • CNN WITH KERAS USING TENSORFLOW BACKEND

  1. Dataset, FER2013 from Kaggle

  2. Construct CNN with Keras using TensorFlow backend

  3. Train the model from the given dataset

  4. Face detection using Caffe based pre-trained deep learning model. Refer https://www.pyimagesearch.com/2018/09/24/opencv-face-recognition/

  5. Real-time emotion recognition and plot animated matplotlib graph from the output.

Heart rate detection

cd Heart rate measurement
python GUI.py

Author : https://github.com/habom2310/Heart-rate-measurement-using-camera

Principle

  • DETECTING CHANGES IN SKING COLOR DUE TO BLOOD CIRCULATION AND CALCULATE HEART-RATE

  1. Face detection using dlib library and get the Region Of Interest(ROI)

  2. Apply a band pass filter to eliminate a range of frequencies

  3. Average colour value of the ROI calculated and pushed to a data buffer

  4. Apply Fast Fourier Transform to the data buffer. Highest peak is the heart-rate

Nvidia Jetson Nano

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GPU: 128-core NVIDIA Maxwell™ architecture-based GPU

CPU: Quad-core ARM® A57

Video: 4K @ 30 fps (H.264/H.265) / 4K @ 60 fps (H.264/H.265) encode and decode

Camera: MIPI CSI-2 DPHY lanes, 12x (Module) and 1x (Developer Kit)

Memory: 4 GB 64-bit LPDDR4; 25.6 gigabytes/second

Connectivity: Gigabit Ethernet

OS Support: Linux for Tegra®

Module Size: 70mm x 45mm

Developer Kit Size: 100mm x 80mm

Install TensorFlow: https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html

  • To work with Jetson nano add below script in detail_main.py, this will ensure the script run on CPU. (NB: Running on GPU will throw CUDA error)
os.environ('CUDA_VISIBLE_DEVICES') = '-1'
  • Install dependencies and connect usb microphone or USB blueotooth dongle

Reference

  1. https://github.com/habom2310/Heart-rate-measurement-using-camera
  2. Real Time Heart Rate Monitoring From Facial RGB Color Video Using Webcam by H. Rahman, M.U. Ahmed, S. Begum, P. Funk
  3. Remote Monitoring of Heart Rate using Multispectral Imaging in Group 2, 18-551, Spring 2015 by Michael Kellman Carnegie (Mellon University), Sophia Zikanova (Carnegie Mellon University) and Bryan Phipps (Carnegie Mellon University)
  4. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation by Ming-Zher Poh, Daniel J. McDuff, and Rosalind W. Picard
  5. Camera-based Heart Rate Monitoring by Janus Nørtoft Jensen and Morten Hannemose
  6. Graphs plotting is based on https://github.com/thearn/webcam-pulse-detector https://www.pyimagesearch.com/2017/04/03/facial-landmarks-dlib-opencv-python/
  7. Heart rate detection on https://www.pyimagesearch.com/2018/09/24/opencv-face-recognition/
  8. Sound recognition on https://github.com/FIGLAB/ubicoustics

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