# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 2 Related Work  
In recent years, activities such as passive human detection [20, 23], counting [24, 26], and motion tracking [27] using BLE have been investigated in the literature, although in most cases the described approaches obtained lower accuracy than their WiFi counterparts [6, 28].  
In [24], a BLE-based human sensing architecture is proposed that is capable of counting the occupants of a lecture room. With 24 beacons placed under the seats in the room and four receivers positioned on the ceiling laterally to the seats, the detection accuracy was higher than 95%, while the root mean square error (RMSE) of the predicted number of occupants was \(\approx\)5.42. Providing only the RMSE of the count measure is not sufficient to assess the performance of the proposed approach, as the average number of occupants is not reported, and therefore, it is not possible to evaluate the fraction (that is, percentage) of miscounted occupants with respect to the real number of occupants. Furthermore, the proposed method relies mainly on the attenuation of the BLE RSSI Line of Sight (LoS) signal on the receiving side due to direct occlusion by the people's bodies, so it is not clear how it would perform if a different deployment of transmitters and receivers is enforced by environmental constraints. Finally, the proposed method requires the manual measurement of the distances between each transmitter and each receiver.  
In [26], a classroom environment is examined where a Radial Basis Function Neural Network is employed to classify RSSI samples. These samples are generated by strategically placed BLE beacons and received by a BLE receiver, with the number of occupants in the room varying. However, the dataset used in the experiments is not publicly accessible, and the paper lacks sufficient detail to allow for replication of the study.  
In [22], a reinforcement learning approach is introduced for passive human detection using BLE RSSI data (without performing occupancy counting). The system leverages feedback from \(CO_{2}\) sensors and other IoT devices to guide the learning process. While the method is described as adaptive to environmental changes, it requires an initial fine-tuning phase. Moreover, its performance is comparable to that of simpler statistical methods, such as the one presented in [20].  
In [25], a BLE-based passive human counting system is evaluated against state-of-the-art WiFi-based alternatives. The proposed architecture, however, necessitates manual measurement of distances between transmitters and receivers during the training phase. Additionally, optimal performance is achieved only when the sensing network operates at very high sampling rates (up to 200 Hz), which significantly exceeds the standard advertisement rate of typical BLE beacons. Moreover, the experimental results presented only take into account up to five persons in a room, and in most cases, only up to two. As a consequence, it is not clear how the model scales with the number of occupants.  
The approach proposed in this paper does not rely on the measurement of the transmitter-receiver distances nor on specific LoS signal occlusion. Moreover, it does not require a full model retraining for each unknown environment, but only a short fine-tuning and, furthermore, has been tested with a number of occupants that varied with a step of 1 from 0 to 10, thus allowing a precise estimation of its performance.