基于交叉槽天线和人工智能集成的手部位置监测及手部摆动分析

Shilpa Pavithran;Vineeta V Nair;Aravind S;Elizabeth George;Alex James
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引用次数: 0

摘要

这项工作详细介绍了使用两个类似的交叉槽天线在2-3 GHz频率范围内工作对人手的位置监测。在这里,一根天线放在胸前,另一根放在志愿者的手上,以监测他们的手部摆动活动。测量的传输值($S_{21}$)以及来自天线的相应频率用于使用自定义生成对抗网络(gan)生成合成$S_{21}$数据。通过人工神经网络(ANN)、支持向量机(svm)、决策树(DT)和随机森林对两个位置的数据进行分类。人工神经网络的准确率达到85%,并在FPGA上实现。本文还讨论了电磁波在人体躯干周围的传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Position Monitoring of Human Hands Using Cross-Slot Antennas and AI Integration for Hand Swing Activity Analysis
This work details the position monitoring of human hands using two similar cross-slot antennas operating in the frequency range of 2–3 GHz. Here, one antenna is kept on the chest and the other one is kept on the hand of a volunteer to monitor hand swing activity. The measured transmission values ($S_{21}$) along with corresponding frequencies from the antenna are used for generating synthetic $S_{21}$ data using custom generative adversarial networks (GANs). Classification of data for two positions is performed on an artificial neural network (ANN), support vector machines (SVMs), decision tree (DT), and random forest. ANN gives an accuracy of 85% and is implemented on FPGA. The article also discusses the electromagnetic (EM) wave propagation around the human torso.
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