MicroNAS: An Automated Framework for Developing a Fall Detection System
This work addresses the challenge of deploying AI models on resource-constrained microcontrollers for real-time fall detection in lower-limb amputees, representing an incremental improvement in domain-specific applications.
The authors tackled the problem of developing efficient neural networks for microcontrollers with limited memory by introducing MicroNAS, an automated neural architecture search tool that optimizes models based on memory constraints, resulting in higher F1-scores for a fall detection system compared to alternative methods like ensemble approaches and H2O Automated Machine Learning.
This work presents MicroNAS, an automated neural architecture search tool specifically designed to create models optimized for microcontrollers with small memory resources. The ESP32 microcontroller, with 320 KB of memory, is used as the target platform. The artificial intelligence contribution lies in a novel method for optimizing convolutional neural network and gated recurrent unit architectures by considering the memory size of the target microcontroller as a guide. A comparison is made between memory-driven model optimization and traditional two-stage methods, which use pruning, to show the effectiveness of the proposed framework. To demonstrate the engineering application of MicroNAS, a fall detection system (FDS) for lower-limb amputees is developed as a pilot study. A critical challenge in fall detection studies, class imbalance in the dataset, is addressed. The results show that MicroNAS models achieved higher F1-scores than alternative approaches, such as ensemble methods and H2O Automated Machine Learning, presenting a significant step forward in real-time FDS development. Biomechanists using body-worn sensors for activity detection can adopt the open-source code to design machine learning models tailored for microcontroller platforms with limited memory.