使用无源RFID标签监测头部方向

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Guilherme Figueiredo;Brandon Hubbs;Adarsh D. Radadia
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引用次数: 0

摘要

本文介绍了一种用于头部方向跟踪的RFID系统(R-SHOT),这是一种新颖的无线和无电池的头部运动学监测方法。R-SHOT使用安装在金属框架背包上的两个线性极化天线,三个垂直放置在受试者头部的商用RFID标签,一个RFID读取器,用于数据收集和处理的软件,以及用于校准的IMU。使用不同偏航、俯仰和侧滚的固定头部位置收集训练数据集,然后使用这些数据建立二阶多变量模型来预测欧拉角(R2 = 0.997,标准误差= 1-3°)。尝试使用一阶模型、减少变量和增加静态头部位置的数量进行模型训练并没有产生有利的结果。利用卡尔曼滤波和线性插值克服了由于噪声和异步采样引起的模型开发挑战。使用静态头部位置开发的R-SHOT模型被发现可以预测欧拉角-当应用于整个头部运动时-误差低,特别是当头部位置接近极端时。该模型的开发是R-SHOT未来实时应用于患者护理和移动辅助解决方案的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring Head Orientation Using Passive RFID Tags
This paper describes an RFID system for head orientation tracking (R-SHOT), a novel wireless and battery-free approach for monitoring head kinematics. R-SHOT uses two linear polarized antennas mounted on a metal frame backpack, three commercial RFID tags placed orthogonally on the subject’s head, an RFID reader, software for data collection and processing, and an IMU for calibration. Training datasets were collected using stationary head positions with varying yaw, pitch, and roll, which were then used to develop a second-order multi-variate model to predict the Euler angles (R2 = 0.997 and standard error = 1–3°). Attempts to use a first-order model, reduce variables, and increase the number of static head positions for model training did not yield favorable results. Challenges in model development due to noise and asynchronous sampling were overcome using a Kalman filter and linear interpolation. The R-SHOT model developed using static head positions was found to predict Euler angles - when applied to full head motion - with low error, especially when the head position was closer to an extreme. The development of this model holds the keys to future real-time application of R-SHOT for patient care and mobility aid solutions.
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CiteScore
5.70
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