During this pandemic, we always wish, if older/blind community have a companion to check their health status. They are more vulnerable to this disease and are little known to modern mobile applications/web applications etc. To give them an alert when the heartbeat is above 100 bpm if they cough frequently give a notification and check their emotional status(so that they won't feel depressed). We made this bot for anyone who want to check their health-status and also get their questions answered, they can simply ask to CUTY. We made This AI partner cheap and affordable to the global community.
What it does
The bot constantly analyzes your face. Using computer vision and deep learning it will identify the strongest emotion and the current heart-rate,we set the heart-rate range 60-100 if it exceeds normal, cuty will identify it, along with that using an analog inbuilt microphone it recognizes the cough and counts it. Everything is save it as a plain text file and with a push of a button, CUTY will say, Good morning sir, from the facial analysis you look happy and have a score of 8. Your heart rate is 62 beats per minute and it is normal, you have a moderate cough, and the cough count up till now is 15. You can connect CUTY with your bluetooth audio system. CUTY is completely offline, everything happens in the edge, which means that it is more secure. She can be mounted anywhere, on your desk, the table you are working, or near your laptop/TV.
How we are building it
Hardware- AI single Board computer, 5 MP camera, analog/bluetooth audio system, 10000 mAh battery, 3.3V to 5V DC-DC boost converter, push button
Programming language- Python
ML architectures/Frameworks -TensorFlow, Caffe
API - Keras
Libraries -TensorFlow, dlib, opencv, scipy(signal processiong) Deep learning techniques- Face detection(caffe), emotion recognition(Keras with TensorFlow backned), Sound detection(Keras with TensorFlow backend)
Principle: detecting changes in sking color due to blood circulation and calculate heart-rate
Face detection using dlib library and get the Region Of Interest(ROI)
Apply a band pass filter to eliminate a range of frequencies
Average colour value of the ROI calculated and pushed to a data buffer
Apply Fast Fourier Transform to the data buffer. Highest peak is the heart-rate
Principle: convolutional neural network(CNN) with Keras using TensorFlow backend
Used transfer learning on the VGG-16 architecture Pre-trained on YouTube-8M for audio recognition
Save the keras model and used for real-time prediction
Principle: CNN with Keras using TensorFlow backend
Dataset, FER2013 from Kaggle
Construct CNN with Keras using TensorFlow backend
Train the model from the given dataset
Face detection using Caffe based pre-trained deep learning model
Real-time emotion recognition and plot animated matplotlib graph from the output.
Challenges we ran into
Building the hardware system is one hell of a job. The entire program should run with a push button, means that after GPIO configuration three program ( cough recognition and counting, emotional recognition, heartbeat detection) should be multiprocessing and save the results in a single text file so that AI text to speech engine can convert to audio.
To make a wonderful case for the system also a challenge we faced.
Demo video editing is a tough job since we used open-sourced video editing software such as Kdenlive and olive for editing.
What have you built during this weeekend
Almost everything we made happens during this weekend. From programming to prototype, we connected CUTY with Bluetooth headset. Now you can hear CUTY even you are busy in your schedules, we made a customized AI- Single board computer that helps her to work fast. Built a wonderful case so that it looks neat. Updates: We made her small enough to fit in the palm, integrated Bio-BERT NLP engine, that is now she can hear you and reply to your query.
The value of the solution after the crisis
She can help anyone who needs to check their physical health, emotional health in real-time. This helps keep track of their overall health status during the regular work. We made CUTY exclusively for the community.
How can we scale it
Since SBC's are cheap and has a plenty in the market, same in the case of batteries, audio system and the case. Funding will helps us to make thousands.
What's next for CUTY- your AI assistant
Make a wonderful body for CUTY, we made the basic prototype on aluminium.
Integrating Alexa/Siri like systems in to CUTY
Integrating Natural Language Processing(NLP) & Natural Language Understanding(NLU) in CUTY(updates: Integrated Bio-BERT in to CUTY)
Since CUTY can be connected to internet via WiFi and Ethernet, she can also be updated via online. We need to use a secured service for that. For that reason we didn't made her online yet, everything from deep learning prediction, analysis, text to speech happens in offline and in the edge but in future we will connect her to a secure server
CUTY uses Reinforcement Learning(RL) to improve herself, but this might be a little tough to handle, considering her computational capacity. Optimized Q-learning techniques are also under research.
Using 3D Convolutional Neural Network for detection of heart rate. As of now CUTY uses computer vision and signal processing techniques such as Fast Fourier Transform(FFT) to detect heart rate. She is accurate as we tested against standard results, but the accuracy can be improved by integrating 3D CNN along with computer vision.
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