Faculty of Electrical Engineering and Information Technology, University of Zilina, 01026 Zilina, Slovakia.
Sensors (Basel). 2020 Dec 31;21(1):206. doi: 10.3390/s21010206.
Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.
褥疮是长期卧床的医院或临终关怀患者可能面临的严重问题之一。为了预防卧床褥疮，我们提出了一种使用新型纺织压力传感器检测卧床者姿势的智能物联网 (IoT) 系统。要构建这样的系统，需要使用不同的技术和技巧。我们使用了六十四个基于导电纱和 Velostat 的新型纺织压力传感器来收集卧床者的压力分布信息。使用消息队列遥测传输 (MQTT) 协议和基于 Arduino 的硬件，我们将测量数据发送到服务器。在服务器端，有一个负责数据收集、评估和配置的 Node-RED 应用程序。由于计算复杂性，我们在单独的设备上使用神经网络来对主体的卧床姿势进行分类。我们从二十一个人在四种卧床姿势下的观察中创建了具有挑战性的数据集。我们在第四类（右侧姿势类型）中实现了 92%的最佳分类精度，而在第一类（仰卧姿势类型）中则获得了最佳的召回率（91%）。在第一类（仰卧姿势类型）中，最佳 F1 分数（84%）。分类后，我们将信息发送到员工桌面应用程序。该应用程序会在需要为个别患者更换卧床姿势时提醒员工，从而预防褥疮。
