CSI crowd-counting: An experimental study using Machine Learning and Deep Learning Algorithms

In 2020 due to the pandemic of COVID-19, we needed to adapt to the situation and control the amount of people inside buildings to prevent the spread of the virus. Crowd-counting using WiFi is a good approach considering the WiFi ubiquity. This paper compares the performance of different Machine Learning and Deep Learning algorithms for measuring the occupancy level of the room by using WiFi signals, e.g., Naive Bayes, K-Nearest Neighbor (KNN), Linear Discriminant Classifier (LDC), Quadratic Discriminant Classifier (QDC), Support Vector Machines (SVM), and 1 Dimension Convolutional Neural Network (1DCNN), obtaining the best accuracy of 91.67% using SVM. In addition, we compare the performance by counting the number of people inside the room, with an accuracy of 93.41% applying an SVM strategy.