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Article

People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms

1
National Research Council of Italy (CNR), Institute of Electronics, Computer and Telecommunication Engineering (IEIIT), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
2
Politecnico di Milano, Department of Management, Economics and Industrial Engineering (DIG), Piazza Leonardo da Vinci 32, 20133 Milano, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Kianoush, S.; Savazzi, S.; Nicoli, M. Device-free Crowd Sensing in Dense WiFi MIMO Networks: Channel Features and Machine Learning Tools. In Proceedings of the 2018 15th Workshop on Positioning, Navigation and Communications (WPNC’18), Bremen, Germany, 25–26 October 2018; pp. 1–6.
Sensors 2019, 19(16), 3450; https://doi.org/10.3390/s19163450
Received: 27 May 2019 / Revised: 24 July 2019 / Accepted: 31 July 2019 / Published: 7 August 2019
(This article belongs to the Section Sensor Networks)
Subject counting systems are extensively used in ambient intelligence applications, such as smart home, smart building and smart retail scenarios. In this paper, we investigate the problem of transforming an unmodified WiFi radio infrastructure into a flexible sensing system for passive subject counting. We first introduce the multi-dimensional channel features that capture the subject presence. Then, we compare Bayesian and neural network based machine learning tools specialized for subject discrimination and counting. Ensemble classification is used to leverage space-frequency diversity and combine learning tools trained with different channel features. A combination of multiple models is shown to improve the counting accuracy. System design is based on a dense network of WiFi devices equipped with multiple antennas. Experimental validation is conducted in an indoor space featuring up to five moving people. Real-time computing and practical solutions for cloud migration are also considered. The proposed approach for passive counting gives detection results with 99% average accuracy. View Full-Text
Keywords: crowd sensing; MIMO WiFi; machine learning; 5G; cloud computing crowd sensing; MIMO WiFi; machine learning; 5G; cloud computing
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MDPI and ACS Style

Kianoush, S.; Savazzi, S.; Rampa, V.; Nicoli, M. People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms. Sensors 2019, 19, 3450. https://doi.org/10.3390/s19163450

AMA Style

Kianoush S, Savazzi S, Rampa V, Nicoli M. People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms. Sensors. 2019; 19(16):3450. https://doi.org/10.3390/s19163450

Chicago/Turabian Style

Kianoush, Sanaz, Stefano Savazzi, Vittorio Rampa, and Monica Nicoli. 2019. "People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms" Sensors 19, no. 16: 3450. https://doi.org/10.3390/s19163450

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