Smart eHealth applications deliver personalized and preventive digital healthcare services to clients through remote sensing, continuous monitoring, and data analytics. Smart eHealth applications sense input data from multiple modalities, transmit the data to edge and/or cloud nodes, and process the data with compute intensive machine learning (ML) algorithms. Run-time variations with continuous stream of noisy input data, unreliable network connection, computational requirements of ML algorithms, and choice of compute placement among sensor-edge-cloud layers affect the efficiency of ML-driven eHealth applications. In this chapter, we present edge-centric techniques for optimized compute placement, exploration of accuracy-performance trade-offs, and cross-layered sense-compute co-optimization for ML-driven eHealth applications. We demonstrate the practical use cases of smart eHealth applications in everyday settings, through a sensor-edge-cloud framework for an objective pain assessment case study.
翻译:智能电子保健应用通过遥感、连续监测和数据分析,向客户提供个性化和预防性的数字保健服务。智能电子保健应用从多种模式中感知输入数据,将数据传送到边缘和/或云节,并用计算集成机学习算法处理数据。 连续循环的噪音输入数据、不可靠的网络连接、ML算法的计算要求和在传感器-边缘层中选择计算位置的运行时间变异影响到由ML驱动的电子保健应用的效率。在本章中,我们介绍了优化配置、探索准确性-性能取舍和跨层感官对ML驱动电子保健应用进行共同优化的边中心技术。我们通过一个传感器-边际框架,为客观疼痛评估案例研究展示了智能电子保健应用在日常环境中的实际应用案例。