论文标题
小型嵌入式系统的传感器数据分类的人工神经网络
Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems
论文作者
论文摘要
在本文中，我们研究了机器学习用于解释传感器模块中测得的传感器值的用法。特别是，我们分析了具有几千字节内存的低成本微型控制器上人工神经网络（ANN）的潜力，以通过传感器捕获的语义丰富数据。重点是将时间数据系列分类为具有高度可靠性。考虑到饲料前向神经网络（FFNN）和经常性神经网络（RNN），分析了ANN的设计和实施。在8位微控制器上的光学手势识别的案例研究中，我们验证了开发的ANN。对于具有两个层和1493个参数的FFNN的最佳可靠性，需要执行时间为36 ms。我们提出了一个工作流程，以开发嵌入式设备的ANN。
In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost micro-controllers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a high level of reliability. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs). We validate the developed ANNs in a case study of optical hand gesture recognition on an 8-bit micro-controller. The best reliability was found for an FFNN with two layers and 1493 parameters requiring an execution time of 36 ms. We propose a workflow to develop ANNs for embedded devices.
