363
views
0
recommends
+1 Recommend
1 collections
    4
    shares
       
      • Record: found
      • Abstract: found
      • Conference Proceedings: found
      Is Open Access

      A Lightweight Classification Algorithm for Human Activity Recognition in Outdoor Spaces

      proceedings-article

      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

      Bookmark

            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.

            Content

            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
            0d89fb04-87ef-4810-aaa4-3f1baaf4e2b3
            © 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

            REFERENCES

            1. “Big Data’s Impact in the World - The New York Times,” The New-York Times 2012 [Online]. Available: http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html. [Accessed: 04-Apr-2018]

            2. “Deep Learning for Sensor-based Activity Recognition: A Survey,” 1 10 2017

            3. “Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies But advanced economies still have higher rates of technology use.”

            4. Ofcom “Digital divide narrows, but 1.1m UK homes and businesses cannot get decent broadband - Ofcom,” 2017 [Online]. Available: http://www.ofcom.org.uk/about-ofcom/latest/media/media-releases/2017/connected-nations-digital-divide. [Accessed: 04-Apr-2018]

            5. “A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation,” IEEE Biomed J.. Heal. Informatics 20 3 893 901 2016

            Comments

            Comment on this article