Overview of RFID Applications Utilizing Neural Networks

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-10-18 DOI:10.1109/JRFID.2024.3483197
Barrett D. Durtschi;Andrew M. Chrysler
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Abstract

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. After the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.
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利用神经网络的 RFID 应用概述
随着射频识别(RFID)方法不断向更高的复杂度发展,一种机器学习的形式正在出现。神经网络(NN)在 RFID 领域的应用正在稳步增加,在定位和活动识别领域,各种研究都取得了可喜的成果。RFID 应用主要分为两类问题,包括回归和分类。我们分析的 RIFD 定位技术属于回归问题,而活动识别属于分类问题。许多作品并没有将自己归类为活动识别方法,但由于它们属于分类范畴,我们仍将其视为活动识别技术。本研究概述了定位领域的神经网络模型,其依据是这些模型是否能独立于测试环境。在活动识别和附件领域,主要方法包括基于标签和无标签方法。在对模型进行调查后,还进行了比较研究,以探讨不同神经网络模型之间提高准确性的原因。
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CiteScore
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