Containerized Wearable Edge AI Inference Framework in Mobile Health Systems

Lionel Nkenyereye, Boon Giin Lee, Wan-Young Chung

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review


The proliferation of wearable devices and personal smartphones has promoted smart mobile health (MH) technologies. The MH applications and services are extremely responsive to computation latency. Edge computing is a distinguished form of cloud computing that keeps data, applications, and computing power away from a centralized cloud network or data center. In this work, we design a containerized wearable edge AI inference framework. The cloud computing layer includes two cloud-based infrastructures: The Docker hub repository and the storage as service hosted by Amazon web service. The Docker containerized wearable inference is implemented after training a Deep Learning model on open data set from wearable sensors. At the edge layer, the Docker container enables virtual computing resources instantiated to process data collected locally closer to EC infrastructures. It is made up of a number of Docker container instances. The containerized edge inference provides data analysis framework (DAF) targeted to fulfill prerequisites on latency, and the availability of wearable-based edge applications such as MH applications.
Original languageEnglish
Title of host publicationIntelligent Human Computer Interaction
Subtitle of host publication15th International Conference, IHCI 2023, Daegu, South Korea, November 8–10, 2023, Revised Selected Papers, Part II
EditorsBong Jun Choi, Dhananjay Singh, Uma Shanker Tiwary, Wan-Young Chung
Place of PublicationCham
PublisherSpringer, Cham
ISBN (Electronic)9783031538308
ISBN (Print)9783031538292
Publication statusPublished - 29 Feb 2024

Publication series

NameLecture Notes in Computer Science
Volumevol 14532


  • Wearable sensors
  • AI inference
  • Edge intelligence
  • Activity recognition
  • Data processing
  • Deep Learning
  • Docker Container


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