William Taylor

TL;DR: This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method and produces a dataset that contains patterns of radio wave signals obtained using software-defined radios to establish if a subject is standing up or sitting down as a test case.

TL;DR: This study outlines the advantages and disadvantages and a breakdown of the methods applied in the current state-of-the-art approaches to detect COVID-19 and highlights some future research directions, which need to be explored further to produce innovative technologies to control this pandemic.

TL;DR: Radar images to detect large scale body movements is looked at and results showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data.

TL;DR: In this paper, a quasi-real-time classification of standing or sitting states using a machine learning model was presented. But, the accuracy of the model was only 96.70 % using the Random Forest algorithm.

TL;DR: In this paper, different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long shortterm memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification.