We present an object motion detection system using backscattered signal strength of passive UHF RFID tags as a sensor for providing information on the movement and identity of work objects-important cues for activity recognition. For using the signal strength for accurate detection of object movement we propose a novel Markov model with continuous observations, RSSI preprocessor, frame-based data segmentation, and motion-transition finder. We use the change of backscattered signal strength caused by tag's relocation to reliably detect movement of tagged objects. To maximize the accuracy of movement detection, an HMM-based classifier is designed and trained for dynamic settings, and the frequency of transitions between stationary/moving states that is characteristic for different object types. We deployed a RFID system in a hospital trauma bay and evaluated our approach with data recorded in the trauma room during 28 simulated resuscitations performed by trauma teams. Our motion detection system shows 89.5% accuracy in this domain.

中文翻译：

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基于无源 UHF RFID 标签的物体运动检测，使用基于隐马尔可夫模型的分类器。

我们提出了一个物体运动检测系统，它使用无源 UHF RFID 标签的反向散射信号强度作为传感器，用于提供有关工作物体的运动和身份的信息——这是活动识别的重要线索。为了使用信号强度准确检测物体运动，我们提出了一种具有连续观察、RSSI 预处理器、基于帧的数据分割和运动转换查找器的新型马尔可夫模型。我们利用标签重定位引起的反向散射信号强度的变化来可靠地检测被标记物体的移动。为了最大限度地提高运动检测的准确性，基于 HMM 的分类器被设计和训练用于动态设置，以及不同对象类型特有的静止/运动状态之间的转换频率。我们在医院创伤室部署了一个 RFID 系统，并在创伤小组进行的 28 次模拟复苏期间使用创伤室中记录的数据评估了我们的方法。我们的运动检测系统在该领域显示出 89.5% 的准确率。