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.

Highlights

  • Development in the Internet of Things means the world is quickly filling up with digital sensors measuring everything from location, movement to humidity [1]

  • Current techniques for data analysis such as machine learning or artificial intelligence are excellent at making sense of all this data

  • This analysis comes with a high computational cost

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Summary

Introduction

Development in the Internet of Things means the world is quickly filling up with digital sensors measuring everything from location, movement to humidity [1]. This is increasing the amount of data generated in the world. Current techniques for data analysis such as machine learning or artificial intelligence are excellent at making sense of all this data. This analysis comes with a high computational cost. Machine learning has the drawback of needing the model to be trained on a data set before accurate results can be given. Collecting enough data to train a model is often time consuming and expensive to complete [2]

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