The Human Stress Detection project utilizes machine learning techniques, various Python libraries (streamlit, numpy, pandas and scikit_learn), and multiple parameters to accurately detect and analyze stress levels in individuals. By combining signals such as heart rate, skin conductance, sleeping hours, blood oxygen, etc. the project employs advanced algorithms to provide stress assessment, offering valuable insights for timely intervention and support.
The stress detection system collects data from multiple sources. The collected data is preprocessed to extract relevant features and eliminate noise. Machine learning algorithm (Decision Tree Classifier) analyze the extracted features to identify patterns associated with stress. The system assigns a stress level score based on the analyzed data. The results are presented to the user through a user-friendly interface.
To run the Stress Detection Project on your local machine, follow these steps:
- Clone the repository:
git clone https://github.com/raviroyal18/Human_Stress_Detection
- Install the required dependencies:
pip install -r requirements.txt- Launch the application:
streamlit run .\main.pyHome Page
Datainfo Page
Detection Page
We would like to acknowledge the following resources and libraries that have greatly contributed to the development of this project:
Contributions are always welcome!
