electronics Article Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum Ramon F. Brena 1, * , Edgar Escudero 1,2 , Cesar Vargas-Rosales 1 , Carlos E. Galvan-Tejada 3 and David Munoz 1   Citation: Brena, R.F.; Escudero, E.; Vargas-Rosales, C.; Galvan-Tejada, C.E.; Munoz, D. Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum. Electronics 2021, 10, 315. https://doi.org/10.3390/ electronics10030315 Academic Editor: Matjaz Gams Received: 19 December 2020 Accepted: 22 January 2021 Published: 29 January 2021 Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). 1 Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501 Sur, Monterrey 64849, Nuevo León, Mexico; A00777396@itesm.mx (E.E.); cvargas@tec.mx (C.V.-R.); dmunoz@itesm.mx (D.M.) 2 Aerobit Technologies, Av. Eugenio Garza Sada 3820, Monterrey 64780, Nuevo León, Mexico 3 Unidad Académica de Ingeniería Eléctrica y Comunicaciones, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Zacatecas Centro 98000, Zacatecas, Mexico; ericgalvan@uaz.edu.mx * Correspondence: ramon.brena@tec.mx Abstract: Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links. Keywords: Wi-Fi; CSI; crowd counting; Doppler spectrum 1. Introduction In recent years, mainly due to the COVID-19 health crisis in 2020 and beyond, the im- portance of technology capable of providing assistance to assess safety in crowds [1–4] has been brought to mainstream awareness [5]. However, crowd assessment applications are not limited to those that provide support for safety, and a new set of applications have been envisioned in businesses [6,7], and in other practical scenarios [8,9]. Of particular interest to the scientific community is the passive and device-free (meaning that the people who are monitored do not need to carry a device such as a cellular phone) estimation of the number of people in a given area. It is important to know the number of people in a room, to monitor human queues or to track the volume of customers in a commercial location, to provide valuable information in the context of smart space design, consumer marketing and venue security [10–12]. Though some recent and some decades-old developments have used computer vision for crowd measurement [1,13,14], nowadays visible light sensors are used with limitations due to the need of a line-of-sight which is subject to variable lighting conditions and coverage, as well as privacy concerns. Recently, the increasing availability and descending costs of Wi-Fi equipment has promoted its use even in applications other than digital communications, such as indoor location [15,16]. In recent years it has begun to be the case of crowd measurement, given that popular Machine Learning techniques [17] can be used to recognize the disturbance patterns that human bodies produce when placed between a Wi-Fi transmitter and a Electronics 2021, 10, 315. https://dx.doi.org/10.3390/electronics10030315 https://www.mdpi.com/journal/electronics