MicroNAS自动设计小内存设备用的跌倒检测模型
MicroNAS: An Automated Framework for Developing a Fall Detection System
- 基于目标微控制器内存大小搜索最优神经网络结构
- 在ESP32上实现高F1分数，优于集成与自动化机器学习方法
- 适合穿戴传感器场景下内存受限设备的模型开发
本文提出MicroNAS，一种专为小型内存微控制器设计的自动化神经架构搜索工具。以具有320 KB内存的ESP32为目标平台，通过将目标设备内存容量作为优化引导，提出新型卷积神经网络与门控循环单元架构的优化方法。对比传统两阶段剪枝方法，验证了该框架在资源受限环境下的有效性。为展示工程应用，以截肢者下肢跌倒检测系统（FDS）为试点，解决数据集类别不平衡问题。结果表明，MicroNAS模型在测试中获得更高F1分数，显著推进实时跌倒检测系统发展。使用可穿戴传感器进行动作识别的生物力学研究者可基于开源代码，在内存受限平台上定制化部署机器学习模型。
原文摘要 · Abstract (English)
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.
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