评估边缘计算和压缩在远程无袖带血压监测中的应用

Ward Goossens, Dino Mustefa, Detlef Scholle, H. Fotouhi, J. Denil
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引用次数: 1

摘要

远程卫生监测系统在卫生保健部门发挥着重要作用。边缘计算是实现这些系统的关键推动者,需要在提供实时保证的同时收集大数据。在这项研究中,我们将重点放在通过心电图(ECG)进行远程无袖带血压(BP)监测上,作为一个案例研究,以评估边缘计算和压缩的好处。首先,我们研究了最先进的BP估计和心电压缩算法。其次,我们开发了一个系统来测量ECG,估计BP,并以三种不同的配置将结果存储在云中:(i)边缘估计,(ii)云估计,(iii)压缩传输的云估计。第三,我们从应用程序延迟、传输数据量和功耗方面评估了这三种方法。在一批64个ECG样本的实验中,边缘计算方法将平均应用延迟降低了15%,平均功耗降低了19%,总传输量降低了85%,证实了边缘计算显著提高了系统性能。当网络带宽有限且边缘计算不切实际时,压缩传输被证明是一种替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Edge Computing and Compression for Remote Cuff-Less Blood Pressure Monitoring
Remote health monitoring systems play an important role in the healthcare sector. Edge computing is a key enabler for realizing these systems, where it is required to collect big data while providing real-time guarantees. In this study, we focus on remote cuff-less blood pressure (BP) monitoring through electrocardiogram (ECG) as a case study to evaluate the benefits of edge computing and compression. First, we investigate the state-of-the-art algorithms for BP estimation and ECG compression. Second, we develop a system to measure the ECG, estimate the BP, and store the results in the cloud with three different configurations: (i) estimation in the edge, (ii) estimation in the cloud, and (iii) estimation in the cloud with compressed transmission. Third, we evaluate the three approaches in terms of application latency, transmitted data volume, and power usage. In experiments with batches of 64 ECG samples, the edge computing approach has reduced average application latency by 15%, average power usage by 19%, and total transmitted volume by 85%, confirming that edge computing improves system performance significantly. Compressed transmission proved to be an alternative when network bandwidth is limited and edge computing is impractical.
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