Detecting Stress Levels from PPG Sensor Data using ANN

[harshdeshmukh21]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Detecting Stress Levels from PPG Sensor Data using Neural Networks.

🔴 Aim : The goal of this project is to predict stress levels using features derived from Photoplethysmography (PPG) sensor data by employing Artificial Neural Networks (ANNs).

🔴 Dataset : https://www.kaggle.com/datasets/vinayakshanawad/heart-rate-prediction-to-monitor-stress-level?select=Train+Data

🔴 Approach : This article describes a machine learning approach to predict stress levels using photoplethysmography (PPG) data and heart rate variability (HRV) features. The pipeline includes data preprocessing, feature engineering, training an artificial neural network model, evaluating its performance, and deploying the model as a web application for real-time stress predictions.

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• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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• Approach for this Project :
• What is your participant role? (Mention the Open Source program)

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

@abhisheks008 Can I add this project to this repository. I think it will be a great addition to DL-Simplified

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[harshdeshmukh21]

@abhisheks008 Please have a look.

[abhisheks008]

Hi @harshdeshmukh21 what are the deep learning models you are planning to implement here for this problem statement?

[harshdeshmukh21]

@abhisheks008 I'll be using a Feedforward Neural Network using TensorFlow, consisting of:
Input Layer: With features derived from PPG data.
Hidden Layers: Multiple dense layers with ReLU activation functions.
Output Layer: A softmax layer for classifying stress levels into three categories.

[abhisheks008]

@abhisheks008 I'll be using a Feedforward Neural Network using TensorFlow, consisting of: Input Layer: With features derived from PPG data. Hidden Layers: Multiple dense layers with ReLU activation functions. Output Layer: A softmax layer for classifying stress levels into three categories.

Hi @harshdeshmukh21 you need to implement at least 3 deep learning models for any problem statement. Please update your approach and get back to me ASAP, as the deadline of the GSSOC is today 7 PM IST.

[harshdeshmukh21]

@abhisheks008 I am not doing it for GSSOC. But I'll share the other 2 algorithms very soon.

[abhisheks008]

@abhisheks008 I am not doing it for GSSOC. But I'll share the other 2 algorithms very soon.

Cool then, you can take your time and get back to me.

[harshdeshmukh21]

@abhisheks008 The project will utilise a machine learning pipeline incorporating CNN, LSTM, and Feedforward Neural Networks to predict stress levels from PPG sensor data, including preprocessing, feature engineering, model evaluation.

[abhisheks008]

Assigned @harshdeshmukh21