# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 1 Introduction  
The ability to estimate the number of occupants in an interior space has a variety of practical applications, including the optimization of heating, ventilation and air conditioning (HVAC) or lighting for energy saving, comfort improvement, building automation, and surveillance [1]. It can also be particularly important for the protection of people in the event of natural or man-made disasters and other emergency scenarios [2]. Furthermore, in privacy-sensitive environments such as hospitals, clinics, or laboratories, real-time people counting can support both safety and operational efficiency, provided that the monitoring solutions adopted do not rely on intrusive technologies such as cameras, which may raise privacy concerns and even cause discomfort among workers [3]. Various technologies have been used to detect and count people in indoor settings, ranging from infrared barriers to computer vision to radio frequency (RF) and radar-like sensing [4]. These can be classified according to the requirement that the sensed human carries a device (device-based versus device-less sensing) and the ability of the sensingdevice to both emit and receive the signal used to detect the human (active versus passive sensing) [5]. The choice of approach and technologies adopted to detect, count, or locate human beings in bounded areas and possibly track their movements and activities depends on the specific context and design constraints [6]. For example, tracking an employee by exploiting some miniaturized device attached to their badge is acceptable, as long as adequate information is provided to them for privacy reasons. In contrast, device-based sensing of visitors in public buildings might not be the most convenient approach, unless visitors need to wear a badge for some other reason. Consequently, if the wearing or carrying of a device is not desirable, device-less sensing is preferable [7]. Several methods have also been explored to monitor people in crowded environments by exploiting the signals emitted by smartphones and other handheld/portable devices that people carry with them consistently [8]. In the last decade, the rapid development of machine learning technology and the introduction of stringent privacy protection regulations (e.g., GDPR in the European Union) have generated a great deal of interest in passive human sensing based on RF, referring to different methods adopted to analyze the deformation that an RF signal undergoes when traversing a human body (see Figure 1), and to extract information regarding the presence and activity of humans in the area covered by the analyzed signal [7]. Ultra-wideband radar technology (UWB) has been demonstrated to be very powerful and effective in passively detecting and monitoring people and their vital signs [9; 10; 11] but requires ad hoc devices to be produced and deployed in the monitored environment, which increases the costs and may raise some coexistence issues with the wireless communication infrastructures already installed in the same area. While coexistence issues can be mitigated by exploiting adequate techniques [12], the costs of producing and deploying ad hoc hardware for passive sensing still require a cheap and widely available solution. As a consequence, the application of commercial off-the-shelf (COTS) WiFi technology to passive human sensing has been widely exploited [13; 14; 15], while fewer works have addressed the combination of communication-oriented and radar-oriented technologies [16]. WiFi has the specific ability to provide Channel State Information (CSI), a comprehensive and punctual description of the state of the receiving channel that allows an accurate analysis of the deformation of the received signal, also with respect to its phase and frequency [17]. Furthermore, the high speed of WiFi allows for a high sampling rate, which makes it possible to estimate vital signs [18] and motion parameters [19] of the individuals being monitored.