Abstract
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.
Highlights
Internet of Things (IoT) and new ubiquitous connectivity paradigms beyond 5G have created unprecedented dynamics for opportunistic sensing by exploiting low-cost radio devices [1,2]
This paper proposes the transformation of a dense Multiple–Input Multiple–Output (MIMO) WiFi network into a passive crowd sensing system by exploiting Machine Learning (ML) tools that process multi-dimensional CSI
Not explicitly addressed in this paper, node layout can be optimized during a pre-deployment stage by using, for example, a Cramer-Rao bound based analysis to evaluate the impact of the geometric factor on the inference system performance [21]
Summary
Internet of Things (IoT) and new ubiquitous connectivity paradigms beyond 5G have created unprecedented dynamics for opportunistic sensing by exploiting low-cost radio devices [1,2]. We address the specific problem of passive people counting in an indoor space covered by a network of MIMO WiFi devices. In reference [8], the authors proposed a non-image people counting system based on a Deep Neural Network (DNN) model using fine-grained physical-layer wireless signatures such as WiFi CSI (Channel State Information) data. This paper proposes the transformation of a dense MIMO WiFi network into a passive crowd sensing system by exploiting Machine Learning (ML) tools that process multi-dimensional CSI extracted from different PHY (PHYsical layer) frames, MIMO antennas and sub-carriers.
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