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Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applications

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Abstract

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.

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Authors and Affiliations

  1. Department of Computing, University of Turku, Turku, Finland

    Anil Kanduri & Pasi Liljeberg

  2. Department of Computer Science, University of California, Irvine, CA, USA

    Sina Shahhosseini, Emad Kasaeyan Naeini, Hamidreza Alikhani & Nikil Dutt

  3. Department of Computer Science, University of California, Irvine, Irvine, CA, USA

    Amir M. Rahmani

  4. School of Nursing, University of California, Irvine, CA, USA

    Amir M. Rahmani

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  1. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

  2. New York University Abu Dhabi, Abu Dhabi, Abu Dhabi, United Arab Emirates

    Muhammad Shafique

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Kanduri, A. et al. (2024). Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applications. In: Pasricha, S., Shafique, M. (eds) Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-40677-5_5

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