超低分辨率RF供电加速度计，用于医院病床出口报警
Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits
摘要：
跌倒有严重的后果，在急诊医院和护理老年人的疗养院很普遍。大多数跌倒发生在卧室和床附近。降低跌倒风险的技术干预措施旨在自动监测离床事件，并随后提醒医护人员提供及时的监督。我们观察到，与患者活动相关的频域信息主要存在于非常低的频率中。因此，我们认识到采用低分辨率加速度感测模式的潜力，而不是用传统的MEMS（微机电系统）加速度计供电和感测。因此，我们研究了一种无电池传感模式，具有低成本无线供电的射频识别（RFID）技术，具有方便集成到服装中的潜力，如医院长袍。我们设计并制作了一个基于被动式加速度计的RFID传感器的具体实现-ID-Sensor。传感器设计允许根据患者上身的运动从唯一RFID标签标识符的变化率导出超低分辨率加速度数据。我们研究了两种卷积神经网络架构，用于从原始的仅RFID数据流中学习，并将其性能与具有工程特征的传统浅层分类器进行比较。我们评估了23例住院老年患者的性能。我们第一次证明，据我们所知，i）嵌入在RF供电的ID传感器数据流中的低分辨率加速度数据可以提供一种实用的活动识别方法; ii）使用完全卷积网络架构，可以从原始的仅RFID数据流中有效地学习高度区分的特征。
英文摘要：
Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometer-based RFID sensor embodiment---ID-Sensor---for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.
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