Human Activity Recognition 🏃 🧗 🚶

The objective of the project is to extract Time Series Features for Human Activity Monitoring along with visualizing the plots for different activities and body parts for 15 individuals. (Created in November 2022)

• Task 1:
  • Applying HVG(Horizontal Visibility Graph) and NVG(Natural Visibility Graph) to compute average degree, network diameter, and average path length.
  • Tabulating all the data for different subjects (1-15) and axes for Head and Chest.
  • Creating scatter plots for climbing up, climbing down, walking and running for head and chest in all directions (x,y,z)
• Task 2:
  • Computing Complexity and Permutation entropy for the data in all three directions (x, y, z)
  • Tabulating all the data for different subjects (1-15), axes, Embedded Dimensions (3,4,5,6) , Embedded Delay (1,2,3) and Signal Length(1024,2048,4096)
  • Creating scatter plots for climbing up, climbing down, walking and running for head and chest in all directions (x,y,z)

Dataset

The dataset was obtained from here. The Human Activity data for 15 individuals for Climbing Up, Climbing Down, Walking and Running was used for the project. The project focuses on accelerometer sensor vibration in three directions (x,y,z) for Head and Chest.

🏗️ Built with

👩‍💻 Libraries used

📋 Tasks Performed

• 🔎 Feature Extraction
• 🔁 Data Manipulation
• 📊 Data Visualization
• 📂 Interacting with the data in the system with os library

✍️ Authors

• Sharayu Thosar
• Ankitha Lakshmisha
• Fenil Niteshbhai Savani

🤝 Support

Contributions, issues, and feature requests are welcome!
Give a ⭐️ if you like this project!