GaitTracker is a research project aimed at developing an affordable and portable device to measure, detect, and analyze gait changes. The device uses an accelerometer to collect gait data, which is then processed to extract gait features and identify potential abnormalities. The goal is to create a tool that can be used for early detection of gait problems, personalized gait monitoring, and the creation of gait databases for research and clinical purposes.
- Personalized gait monitoring: Tracks individual gait patterns over time to detect sudden changes or abnormalities.
- Early detection of gait problems: Helps identify potential gait issues early on, allowing for timely intervention and prevention of further complications.
- Gait database creation: Contributes to the building of digital gait repositories for research and clinical applications.
- Affordable and portable: Designed to be accessible and easy to use in various settings, including clinical practices and home environments.
Three studies were conducted to collect gait patterns, resulting in a structured gait database. The raw data format and metadata details are explained, showcasing the potential for creating a digital healthcare repository.
- Access the gait data in the gait-database/dataset folder.
- Review the MATLAB code in gait-analysis/matlab and Python notebooks in gait-analysis/python to understand the data processing and analysis pipeline.
- Execute the MATLAB scripts and Python notebooks to reproduce the results and visualizations.
- Development of a companion mobile application: To make the visualization and analysis of gait data more accessible to end-users.
- Expansion of the study: To include a wider range of gait patterns and collect data from both legs simultaneously.
- Creation of Data Ingestion pipelines for storing the data obtained from multiple devices and real-time data processing