Edge Computing Framework for Wearable Sensor-Based Human Activity Recognition

  • Semir SalkicEmail author
  • Baris Can Ustundag
  • Tarik Uzunovic
  • Edin Golubovic
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 83)


Human activity recognition is done based on the observation and analysis of human behavior to understand the performed activity. With the emergence of battery powered, low cost and embedded wearable sensors, it became possible to study human activity in various real-world scenarios. Together with the development in data collection, novel machine learning based modeling approaches show huge promise in modeling human activities accurately. Edge computing framework, that is capable of executing human activity recognition models at the edge of the network, is presented in this paper. Framework architecture and its implementation on a single board computer are presented. The framework allows the implementation of various machine learning models for human activity recognition in a standardized manner. The framework is demonstrated experimentally.


Human activity recognition Edge computing Wearable sensors Machine learning IoT Neural networks 



The authors would like to thank the Ministry of Civil Affairs of Bosnia and Herzegovina for the financial support provided for this study.

Authors acknowledge Inovatink company for providing technical, logistic and financial support for this study.

Authors would also wish to express gratitude to Bosnia and Herzegovina Future Foundation (BHFF) for providing financial support in the form of a scholarship to lead author of this study.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Electrical EngineeringUniversity of SarajevoSarajevoBosnia and Herzegovina
  2. 2.Department of Computer EngineeringIstanbul Technical UniversityIstanbulTurkey
  3. 3.InovatinkIstanbulTurkey

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