Final project of ETH Machine Learning on Microcontrollers course.
The project is completed in collaboration with @Liux1n. We together proposed several networks for human fall detection based on KFall dataset and implemented them on STM32 and MAX78000 platforms.
The repository is structured as follows:
- Fall_Detection contains code for model training.
- FallDetection-STM32-CUBEAI contains STM32 implementation with CUBEAI runtime.
- FallDetection-STM32-TFLite contains STM32 implementation with TensorFlow Lite runtime.
- FallDetection-MAX78000 contains MAX78000 implementation.
- Yu, X., Jang, J., & Xiong, S. (2021). A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors. Frontiers in Aging Neuroscience, 13. https://doi.org/10.3389/FNAGI.2021.692865
- Koo, B., Yu, X., Lee, S., Yang, S., Kim, D., Xiong, S., & Kim, Y. (2023). TinyFallNet: A Lightweight Pre-Impact Fall Detection Model. Sensors 2023, Vol. 23, Page 8459, 23(20), 8459. https://doi.org/10.3390/S23208459
- Yu, X., Qiu, H., & Xiong, S. (2020). A Novel Hybrid Deep Neural Network to Predict Pre-impact Fall for Older People Based on Wearable Inertial Sensors. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/FBIOE.2020.00063