Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
This work addresses efficiency challenges in smart eHealth applications for personalized healthcare, but it appears incremental as it builds on existing edge computing and optimization methods.
The paper tackles the problem of optimizing multi-modal machine learning-driven eHealth applications by addressing run-time variations like noisy data and unreliable networks, presenting edge-centric techniques for compute placement and accuracy-performance trade-offs, and demonstrating their use in a pain assessment case study.
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.