This repository contains a Jupyter notebook titled "Force and Acceleration Detection" focused on analyzing and visualizing force and acceleration data in physical activities. It is particularly useful for sports scientists, physiotherapists, and fitness enthusiasts interested in understanding the dynamics of physical movements.

• Data Import and Preprocessing: The notebook begins with importing necessary libraries and datasets. It includes preprocessing steps to prepare the data for analysis.
• Data Analysis: The core of the notebook involves analyzing force and acceleration data. It includes calculations of key metrics like Ground Reaction Force (GRF), acceleration of different body parts (foot, shank, thigh), and peak detection in force-time series data.
• Visualization: The notebook provides detailed visualizations of the analyzed data, including time-series plots and peak detection graphs. These visualizations help in understanding the patterns and anomalies in the physical movements.

1. Setup: Clone the repository and ensure that you have Jupyter Notebook installed on your system. Install the required libraries mentioned in the notebook.
2. Running the Notebook: Open the Jupyter notebook and execute the cells in sequence. You can modify the dataset or parameters as per your requirements.
3. Data Customization: The notebook can be adapted to different types of force and acceleration datasets. Adjust the preprocessing and analysis sections accordingly.

• Python 3.x
• Libraries: [list the main libraries used in the notebook, such as Pandas, NumPy, Matplotlib]

Contributions to improve the notebook are welcome. Please follow the standard GitHub pull request process to submit your improvements.

None.

For any queries or discussions regarding the notebook, please raise an issue in the repository.