Psychopathology Fer Assistant

Abstract

1. Introduction

An intelligent assistant platform to track psychopathology patients responses during face-to-face and remote sessions.

Psychopathology Assistant Because mental health matters. View the demo »

This platform makes use of a machine learning algorithm capable of tracking and detecting facial expressions to identify associated emotions through a camera. This allows the corresponding medical staff to take care of their patients by creating medical records supported by the artificial intelligent system, so they can follow-up the corresponding treatments.

2. Objective

[WINNER! 🏆] Psychopathology FER Assistant. Because mental health matters. My project submission for #TFWorld TF 2.0 Challenge at Devpost.

This project demonstrates how Jupyter Notebook, Python, Raspberry Pi can be applied to a real-world Internet of Things (IoT) problem.

4. Technology Stack

• Tensorflow 2.0
• Google Colab (with its wonderful GPUs)
• Model quantization with tf.lite for serving
• A Raspberry Pi Model 3B+
• A real-time Flask and Dash integration (along with Dash Bootstrap Components) +
• A real-time database, of course, from Firebase
• The Kaggle API to get the dataset

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/RodolfoFerro/psychopathology-fer-assistant.git
cd psychopathology-fer-assistant

1. Clone the psychopathology-fer-assistant repo:
2. Install requirements using pip:
3. You may also need to create your own real-time database on Firebase and set the corresponding configuration variables on the app/init.py and rpi/main.py files.

git clone https://github.com/RodolfoFerro/psychopathology-fer-assistant.git

pip install -r requirements.txt

python run.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add a mobile dashboard using Blynk or Firebase
• Store readings in a cloud database for history charts
• Add alerts via SMS / Telegram when thresholds are crossed

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which microcontroller / board and sensors are used and why?
2. How does the device send data (Wi-Fi, MQTT, HTTP, Bluetooth)?
3. Where is the sensor data stored and visualised?
4. How is power consumption managed?
5. How would you secure the device and its communication?

9. Source Code & License

This project is developed by RodolfoFerro and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.