ALERT is an innovative machine learning initiative focused on advancing fall detection capabilities within smart wearable technologies.Its primary mission is to develop a highly accurate, real-time fall detection model, adaptable to various smartwatch platforms.

To create a machine learning model, trained using Python on diverse public datasets, and convert it to a format suitable for implementation in different smartwatch environments. The project emphasizes the development and provision of a versatile model, rather than specific app development for each smartwatch brand.

1. Cross-Platform Model Design: Training a model versatile enough to be adapted to various smartwatch conventions and implementations.
2. Smartwatch Agnostic: The repository focuses on the training aspect and provides the model file, serving as a foundation for further implementation in different smartwatch ecosystems.
3. Data-Driven Approach: Leveraging public fall datasets for comprehensive and accurate model training.
4. Real-Time Detection: Ensuring the model is capable of real-time analysis for immediate fall detection.
5. Open Source Collaboration: Encouraging collaboration and contributions, fostering a community-driven approach to enhance and refine the model.

By providing a robust, adaptable model, ALERT aims to contribute significantly to the enhancement of user safety across various smartwatch platforms. The project's core model can be a crucial tool in developing applications that aid in immediate fall detection and timely assistance, especially for the elderly and high-risk individuals.

• Training: Python, Data Analysis Libraries (e.g., Pandas, Scikit-learn)
• Deployment: Model conversion tools for JavaScript compatibility (e.g., ONNX, ONNX.js)
• Machine Learning: Random Forest (and potentially Neural Networks for larger datasets)

ALERT envisions extending its impact by refining the model for greater accuracy, exploring integration with broader health monitoring systems, and adapting to emerging wearable technologies.

We express our gratitude to the ARCO Research Group for their invaluable contribution to the field of fall detection. Their dedication to making this dataset publicly accessible greatly aids in advancing research and development in this domain.

For more details about the ARCO Fall Detection Dataset, visit their website: ARCO Fall Detection Dataset.

We are grateful to the team behind the FALLALLD dataset for their efforts in compiling and sharing this valuable resource. Their commitment to advancing research in human activity recognition and fall detection is deeply appreciated.

For further details about the FALLALLD dataset, please visit the IEEE DataPort website: FALLALLD: Comprehensive Dataset of Human Falls and Activities of Daily Living.

We extend our sincere thanks to the team responsible for the UMAFall dataset. Their dedication to compiling this dataset and making it publicly available greatly contributes to the advancements in the field of fall detection and prevention.

For more information and to access the UMAFall dataset, please visit the following link: UMAFall Dataset on Figshare.