A Comparative Analysis of Deep Learning and Machine Learning on Detecting Movement Directions Using PIR Sensors

Abstract

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.