Human performance data
Sports Science Diagnostics Project This project aims to provide a comprehensive and data-driven approach to sports science diagnostics by leveraging the power of MATLAB and utilizing the latest scientific articles and research in the fields of exercise physiology, biomechanics, neuromechanics, and more. The goal is to create accurate and reliable models for analyzing various types of data related to sports performance.
Project Overview The project focuses on developing MATLAB scripts and functions that enable the analysis and modeling of sports science data. As of September of 2023 there are two other branches beside the main branch. These two branches are 'CPET' and 'force-plate-data' and each branch purports a project based on the type of data collected. For instance the CPET branch/project data that has been exported from MetaSoft/MetaLyzer in the format of xlsx whereas the 'force-plate-data' project is based off of force plate data. The plan is to provide more extensive projects pertaining to bioemchanics of movements and may include data from motion capture systems, force plates, isokinetic dynamometers, and heart rate variability devices, among others.
- Motion Capture Project (coming up in October 2023).
The key idea for these projects is to share the data that is being collected by myself, a sports science practitioner, then find ways to 1) model the data with the aim of answering research questions, if you happen to be an advanced sports science practitioner, 2) get your practice working with large data sets where you get to work with real life data and practice some basic yet key tools of MATLAB (and any other programming tool) such as loops, iterating, indexing etc.
Key Features Utilization of MATLAB for data analysis, modeling, and visualization. Integration of the most up-to-date scientific articles and research findings in exercise physiology, biomechanics, and neuromechanics. Development of algorithms and models to process and interpret sports science data. Creation of visualizations to enhance the understanding of complex datasets. Application of statistical analysis techniques to identify patterns and trends in the collected data. Collaboration with experts in the field to ensure the accuracy and reliability of the developed models and algorithms.
Getting Started
To get started with this project, please follow these steps:
Clone the repository to your local machine. Ensure that you have MATLAB installed. Install any necessary dependencies or toolboxes required for the project. Open the MATLAB scripts and functions to explore and modify them as needed. Import your own sports science data or use the provided sample datasets for testing and analysis. Refer to the documentation and comments within the code for guidance on how to use and customize the functionality. Contributions Contributions to this project are welcome and encouraged. If you have ideas for improvements or additional features, please feel free to submit a pull request. Additionally, if you come across any issues or bugs, please report them in the issue tracker.
License Feel free to use and modify the code as per the terms of the license.
Acknowledgments We would like to express our gratitude to the scientific community for their valuable research and contributions in the fields of exercise physiology, biomechanics, neuromechanics, and sports science. Without their work, this project would not be possible.
Contact For any questions or inquiries regarding this project, please reach out to leutrim.mehmeti@tum.de We hope this project proves to be a valuable resource for sports scientists, researchers, and enthusiasts in the pursuit of enhancing sports performance through data-driven diagnostics.
Happy coding and sports science exploration!