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.

中文翻译：

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超低分辨率射频供电加速度计，用于在住院患者病床出口时发出警报

跌倒会造成严重后果，在急症医院和照顾老年人的疗养院中很普遍。大多数跌倒发生在卧室和床边。降低跌倒风险的技术干预旨在自动监控离床事件，并随后提醒医护人员及时提供监督。我们观察到与患者活动相关的频域信息主要以非常低的频率存在。因此，我们认识到与传统 MEMS（微机电系统）加速度计供电和传感相比，采用低分辨率加速度传感模式的潜力。最后，我们研究了一种具有低成本无线供电射频识别 (RFID) 技术的无电池传感模式，该技术有可能方便地集成到服装中，例如医院的长袍。我们设计并构建了一个基于无源加速度计的 RFID 传感器实施例---ID-Sensor---用于我们的研究。传感器设计允许根据患者上半身的运动从唯一 RFID 标签标识符的变化率中导出超低分辨率加速度数据。我们研究了两种用于从原始 RFID 数据流中学习的卷积神经网络架构，并将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 名住院老年患者的表现。我们首次并尽我们所知证明：i) 嵌入在射频驱动的 ID-Sensor 数据流中的低分辨率加速度数据可以为活动识别提供一种可行的方法；ii) 可以使用完全卷积网络架构从原始的 RFID-only 数据流中有效地学习高度区分特征。