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IoT-Based Fall and ECG Monitoring System: Wireless Communication System Based Firebase Realtime Database

机译:基于IOT的秋季和心电图监控系统:基于无线通信系统的Firebase实时数据库

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Monitoring elderlies living alone has been a rising issue that caregivers are interested in solving since many elderlies are at risk of experiencing a fall. In the absence of urgent help, serious consequences may occur. This paper presents a complete communication system to monitor elderlies by checking their Electrocardiogram (ECG) and accelerometer data through a cloud-based server anytime on a mobile application ensuring that they are unharmed. This has been implemented by having a Multi-core Processing Unit (MPU), acting as a gateway, at the elderly's side monitoring signals coming from a wearable sensing device. It will classify ECG and accelerometer data using Machine Learning algorithms, stream the data upon request, alert caregivers through a mobile application and store the data on the database for further analysis in case of a fall. Fall detection had an accuracy of 95% using Extended Nearest Neighbor (E-NN) learning algorithm.
机译:仅监测独自生活的老年人一直是一个不断上升的问题,即照顾者对解决的人有兴趣,因为许多老年人都有跌倒的风险。在没有紧急帮助的情况下,可能发生严重后果。本文介绍了通过在移动应用程序的基于云的服务器通过基于云的服务器检查其心电图(ECG)和加速度计数据来监控老人的完整通信系统,确保它们没有受到影响。这通过具有多核处理单元(MPU)来实现,该单核处理单元(MPU)作用为网关,在来自可穿戴传感装置的老年人的侧监测信号处。它将通过机器学习算法对ECG和加速度计数据进行分类,通过移动应用程序来传输数据,通过移动应用程序提醒护理人员并将数据存储在数据库上,以便在跌倒时进一步分析。坠落检测使用扩展最近邻(E-NN)学习算法的精度为95%。

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