Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Brunswick, Germany.
Stud Health Technol Inform. 2023 May 18;302:1002-1006. doi: 10.3233/SHTI230326.
Smart wearables advance to reliably and continuously measure vital signs. Analyzing the produced data requires complex algorithms, which would unreasonably increase the energy consumption of mobile devices and exceed their computing power. Fifth-generation (5G) mobile networks provide low latencies, high bandwidth, and many connected devices and introduced multi-access edge computing, which brings high computation power close to the clients. We propose an architecture for evaluating smart wearables in real-time and evaluate it exemplary with electrocardiography signals and binary classification of myocardial infarctions. Our solution shows that real-time infarct classification is feasible with 44 clients and secured transmissions. Future releases of 5G will increase real-time capability and enable capacity for more data.
智能可穿戴设备不断发展,能够可靠地连续测量生命体征。分析生成的数据需要复杂的算法,这将不合理地增加移动设备的能源消耗,并超过其计算能力。第五代(5G)移动网络提供低延迟、高带宽和许多连接的设备,并引入了多接入边缘计算,将高计算能力带到客户端附近。我们提出了一种用于实时评估智能可穿戴设备的架构,并使用心电图信号和心肌梗死的二进制分类对其进行了示例评估。我们的解决方案表明,通过 44 个客户端和安全传输,可以实现实时的梗死分类。未来的 5G 版本将提高实时能力,并为更多数据提供容量。