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      A Lightweight Classification Algorithm for Human Activity Recognition in Outdoor Spaces

      1 , 1 , 1 , 1 , 2 , 2

      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Lightweight algorithm, Activity recognition, Euclidean distance, Machine learning, Energy efficiency, Low-computational capacity

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          Abstract

          The aim of this paper is to discuss the development of a lightweight classification algorithm for human activity recognition in a defined setting. Current techniques to analyse data such as machine learning are often very resource intensive meaning they can only be implemented on machines or devices that have large amounts of storage or processing power. The lightweight algorithm uses Euclidean distance to measure the difference between two points and predict the class of new records. The results of the algorithm are largely positive achieving accuracy of 100% when classifying records taken from the same sensor position and accuracy of 80% when records are taken from different sensor positions. The outcome of this work is to foster the development of lightweight algorithms for the future development of devices that will consume less energy and will require a lower computational capacity.

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          Most cited references 2

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          “A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation,”

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            “Big Data’s Impact in the World - The New York Times,”

             S. Lohr (2012)
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              Author and article information

              Contributors
              Conference
              July 2018
              July 2018
              : 1-5
              Affiliations
              [1 ] School of Computing, Ulster University, Jordanstown Campus,Shore Road, Newtownabbey,Co. Antrim, BT37 0QB, Northern Ireland
              [2 ] Dublin Institute of Technology, Kevin Street, Dublin, D08 X622, Ireland
              Article
              10.14236/ewic/HCI2018.53
              © McCalmont et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

              This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

              Proceedings of the 32nd International BCS Human Computer Interaction Conference
              HCI
              32
              Belfast, UK
              4 - 6 July 2018
              Electronic Workshops in Computing (eWiC)
              Human Computer Interaction Conference
              Product
              Product Information: 1477-9358BCS Learning & Development
              Self URI (journal page): https://ewic.bcs.org/
              Categories
              Electronic Workshops in Computing

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