计算机科学
边缘计算
可穿戴计算机
量化(信号处理)
边缘设备
人工神经网络
推论
人工智能
实时计算
GSM演进的增强数据速率
嵌入式系统
机器学习
计算机视觉
云计算
操作系统
作者
Jiarong Chen,Mingzhe Jiang,Xianbin Zhang,Daniel Santos da Silva,Victor Hugo C. de Albuquerque,Wanqing Wu
标识
DOI:10.1016/j.eswa.2022.119407
摘要
Implementing internet of things technologies in health monitoring systems attracts a lot of attention. Running the model at edge can continuously and in real-time monitor the user’s physiological information, which can be adopted in universal medical care. However, this task is challenging and rarely discussed due to its limited computational capacities and storage resources. This work propose a novel framework for neural network training on resource-constrained embedded systems. Based on feature engineering, the lightweight neural network is implemented on embedded devices, including the training and testing. By losing some certainty, an ultra-lightweight model can be realized through parameter quantization to get further memory saving and reduce the storage burden of the device. The method is implemented and tested on a cheap AVR-based 8-bit micro-controller for atrial fibrillation detection from electrocardiogram (ECG) features. The results prove the feasibility of 100 samples training on-device with test performance comparable to models developed on a computer. The trained lightweight model can be compressed to about 0.3 of the original size with negligible rebuilt performance loss. It can be combined with wearable devices for chronic diseases and long-term monitoring.
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