Abstract
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.
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Acknowledgements
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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Salkic, S., Ustundag, B.C., Uzunovic, T., Golubovic, E. (2020). Edge Computing Framework for Wearable Sensor-Based Human Activity Recognition. In: Avdaković, S., Mujčić, A., Mujezinović, A., Uzunović, T., Volić, I. (eds) Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019). IAT 2019. Lecture Notes in Networks and Systems, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-24986-1_30
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DOI: https://doi.org/10.1007/978-3-030-24986-1_30
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