mlsanityData to 7 Oct 2026

Edge-centric Optimization of Multi-modal ML-driven eHealth Applications

arXiv 2208.02597 ↗First posted 4 Aug 2022cs.LG cs.DCcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 82 references that are in the map. First posted on arXiv 4 Aug 2022.

Builds on

18 of 82 references.

References · 18

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6 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detectioncited by 16
  2. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  3. 2024Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approachcited by 7