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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.
McCalmont, G., Zheng, H. & Wang, H. (2018). A lightweight classification algorithm for human activity recognition in outdoor spaces. Proceedings of the 32nd International BCS Human Computer Interaction Conference, Belfast, UK, 4 - 6 July. doi:10.14236/ewic/HCI2018.53