Deep Learning for RFID-Based Activity Recognition.
Deep Learning for RFID-Based Activity Recognition.
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DOI：
10.1145/2994551.2994569
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发表时间：
2016-11
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影响因子：
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通讯作者：
Burd RS
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文献类型：
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作者：
Li X;Zhang Y;Marsic I;Sarcevic A;Burd RS
我们提出了一个使用深度卷积神经网络从无源RFID数据进行活动识别的系统。我们直接将RFID数据输入深度卷积神经网络进行活动识别，而不是选择特征并使用级联结构，首先从RFID数据中检测对象的使用，然后预测活动。由于我们的系统将活动识别视为一个多类分类问题，因此对于具有大量活动类的应用程序来说，它是可扩展的。我们使用在创伤室收集的射频识别数据来测试我们的系统，包括来自16个实际创伤复苏的14小时射频识别数据。我们的系统优于现有的活动识别系统，并且在过程阶段检测方面取得了与需要可穿戴传感器或手动生成输入的系统相似的性能。我们还分析了当前用于从RFID数据中识别活动的深度学习架构的优势和局限性。
We present a system for activity recognition from passive RFID data using a deep convolutional neural network. We directly feed the RFID data into a deep convolutional neural network for activity recognition instead of selecting features and using a cascade structure that first detects object use from RFID data followed by predicting the activity. Because our system treats activity recognition as a multi-class classification problem, it is scalable for applications with large number of activity classes. We tested our system using RFID data collected in a trauma room, including 14 hours of RFID data from 16 actual trauma resuscitations. Our system outperformed existing systems developed for activity recognition and achieved similar performance with process-phase detection as systems that require wearable sensors or manually-generated input. We also analyzed the strengths and limitations of our current deep learning architecture for activity recognition from RFID data.