Machine learning has played a significant role in building intelligent systems in the history of data science. In the recent paradigm where objects in the world will be connected with each other, commonly referred to as the Internet of Things (IoT), people begin to consider the challenges and opportunities to utilize the huge data sets generated, also referred to as Big data. One of the active research topics in dealing with the IoT’s big data is the practical feasibility of algorithms used in classical machine learning but also in a newly emerging branch, called deep learning. In this article, we demonstrate a quantitative analysis comparing performance between classical machine learning and deep learning algorithms with a human movement direction detecting application based on analog pyroelectric infrared (PIR) sensor signals. The sensing data acquisition and retrieval system is implemented with the open-source IoT software platforms based on the oneM2M standard. With the analog PIR data sets collected from 30 subjects, we perform experimental studies comparing classical machine learning and deep learning algorithms in terms of economic feasibility, scalability, generality, and real-time detection performance. The results show that classical machine learning shows better performance in real-time detection (i.e., with the sensing values within the first 0.5 s). In contrast, our simple deep learning model achieves about 90% accuracy for detecting moving directions even with the data sets from only three subjects and a single PIR sensor. Moreover, it could be applied to a larger number of subjects without updates.

中文翻译：

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深度学习和机器学习使用PIR传感器检测运动方向的比较分析

在数据科学史上，机器学习在构建智能系统中发挥了重要作用。在最近的范例中，世界上的对象将相互连接，通常称为物联网（IoT），人们开始考虑利用所产生的巨大数据集（也称为大数据）所面临的挑战和机遇。 。处理IoT大数据的活跃研究主题之一是在经典机器学习中使用的算法的实际可行性，在新兴的分支机构深度学习中也是如此。在本文中，我们演示了一种定量分析，将经典机器学习和深度学习算法与基于模拟热释电红外（PIR）传感器信号的人体运动方向检测应用程序之间的性能进行了比较。传感数据采集与检索系统是基于oneM2M标准的开源IoT软件平台实现的。借助从30个主题中收集的模拟PIR数据集，我们进行了实验研究，从经济可行性，可扩展性，通用性和实时检测性能方面比较了经典机器学习和深度学习算法。结果表明，经典机器学习在实时检测中表现出更好的性能（即，传感值在前0.5 s之内）。相比之下，即使使用仅来自三个对象和单个PIR传感器的数据集，我们简单的深度学习模型也可以实现约90％的准确度来检测运动方向。而且，它可以应用于大量主题而无需更新。