BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory

IF 2.7 4区 工程技术 Q2 TRANSPORTATION SCIENCE & TECHNOLOGY
Haiping Wu, Steffen Junginger, Thomas Roddelkopf, Hui Liu, Kerstin Thurow
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引用次数: 0

Abstract

Abstract Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory. Bluetooth low-energy (BLE) beacons have the advantage of low price, small size, and low-energy consumption, which make them a promising solution for sample position estimation in the automated laboratory. Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength (RSS) data. However, most of the research depends on intensive beacon installation. Proximity estimation, which depends entirely on one beacon, is more suitable for sample position estimation in large automated laboratories. The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model (PLM), which is a widely used radio wave propagation model-based proximity estimation method. In this paper, BLE sensing devices for sample position estimation are proposed. The BLE beacon-based proximity estimation is discussed in the framework of machine learning, in which the support vector regression (SVR) is utilized to model the nonlinear relationship between the RSS data and distance, and the Kalman filter is utilized to decrease the RSS data deviation. The experimental results over different environments indicate that the SVR outperforms the PLM significantly, and provides 1 m absolute errors for more than 95% of the testing samples. The Kalman filter brings benefits to stable distance predictions. Apart from proximity-based sample position estimation, the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.
用于生命科学自动化实验室样本位置估计的BLE信标
摘要在生命科学自动化实验室中,样品位置的估计是工作过程监控和管理的关键。蓝牙低功耗(BLE)信标具有价格低、体积小、能耗低的优点,是自动化实验室中样品位置估计的一个很有前途的解决方案。为了利用接收到的信号强度(RSS)数据实现室内定位,提出了几种指纹识别模型。然而,大多数研究都依赖于密集的信标安装。接近估计完全依赖于一个信标,更适合于大型自动化实验室的样本位置估计。生命科学自动化实验室环境的复杂性给传统的路径损耗模型(PLM)带来了挑战,PLM是一种广泛使用的基于无线电波传播模型的接近估计方法。本文提出了用于样本位置估计的BLE传感装置。在机器学习框架下讨论了基于BLE信标的接近估计,利用支持向量回归(SVR)对RSS数据与距离之间的非线性关系进行建模,并利用卡尔曼滤波减小RSS数据的偏差。在不同环境下的实验结果表明,SVR显著优于PLM,在95%以上的测试样本中提供1 m的绝对误差。卡尔曼滤波有利于稳定的距离预测。除了基于接近度的样本位置估计外,所提出的框架在平行工作台之间的位置估计和自动化工作站的位置估计中都是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Transportation Safety and Environment
Transportation Safety and Environment TRANSPORTATION SCIENCE & TECHNOLOGY-
CiteScore
3.90
自引率
13.60%
发文量
32
审稿时长
10 weeks
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