# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 3 BLE-Based Passive Human Sensing  
Radio Frequency (RF)-based passive human sensing (PHS) takes advantage of the modification induced in the RF signal by the presence of a human body along its pathway. When traversing a human body, the radio signal is refracted and attenuated, thus undergoing changes in its phase and intensity (see Figure 2). The signal can also be refracted and reflected by objects, furniture, and walls, thus generating multipath signals. All these types of signals must be captured, filtered, and analyzed for PHS [23]. A general investigation of the effects of reflected and refracted components of the received signal on the accuracy and reliability of the passive detection process was investigated in several seminal works, such as in [29] and is outside the scope of this paper. Unlike the changes induced by the presence of fixed obstacles and signal reflections, which may combine into constructive or destructive interference but in general do not show significant time dynamics, the deformations induced by human bodies change in time according to people's movements and physiological fluctuations, for example, due to respiration and heartbeat. The time dynamics of human-induced deformation of RF signals has been widely investigated, for example, for the detection and analysis of vital signs based on WiFi [18]. The BLE protocol does not support the kind of direct measurement of highly detailed channel information (e.g., CSI) exploited by WiFi-based PHS approaches [6]. However, the Received Signal Strength Information (RSSI) variations contain sufficient information to allow human detection and counting, provided that the RSSI is adequately filtered to remove noise and unwanted variations, and that a reliable predictive model is adopted. In fact, due to frequent changes in pose, location, and physiological dynamics (for example, respiration), the RSSI deformation caused by the presence of a human body is affected by high variability, as shown in Figure 3.  
Furthermore, due to security constraints, the Adaptive Frequency Hopping (AFH) mechanism in BLE prevents the sensing applications from retrieving from the BLE interface the specific channel on which a message was received. Since the RSSI exhibits distinct mean values across different channels [30], frequency hopping introduces significant variability  
Figure 3: Graph of the variations in RSSI in time, for the cases: 0-person occupancy, 5-person occupancy, 11-person occupancy. Note that people in the room moved often and the variability due to people motion increases with the number of persons in the room. Figure 2: Line of Sight (LoS) and Multipath (Non-LoS) PHS. Note that multiple signals traverse the same obtruding human body from different angles and with different time (phase) shifts, thus transporting spatially and temporally diverse information. in the RSSI distribution. This variability negatively impacts the classification accuracy of most BLE-based passive human sensing (PHS) methods. To address this issue, the approach adopted in this paper follows the strategy proposed in [27], wherein the transmitter is configured to operate on a fixed channel. We propose a sensing architecture composed of \(n_{t}\) BLE beacons (the transmitters) and \(n_{t}\) BLE receivers. BLE beacons are low-cost, low-power devices that only broadcast advertising frames, marked with a unique device identifier. In our case, the beacons are set to transmit on a fixed channel that is specified at the configuration time. Receivers are also low-cost, low-power, BLE-enabled devices that receive the advertising frames, add a receiver identifier and a timestamp, and send them as a data stream to a server that analyzes all the received data and produces an occupancy estimate for each area of interest in near-real time. This architecture potentially also allows for passive person localization, but this application is not covered in this paper.