# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 1 Introduction  
Radio Frequency (RF) sensing systems can assess propagation environments and spectrum utilization to improve wireless systems' performance [1]. RF sensing and context awareness systems were limited for specific purposes due to the technical challenges related to the understanding of most diversified contexts, where the techniques for classifying the scenario objects are more demanding [2]. The complexity of such challenges has motivated the adoption of more advanced techniques, including deep learning (DL) [3] and artificial intelligence (AI) techniques [4]. Several machine learning (ML) techniques have been used recently for RF human posture detection based on the RF Time of Flight (ToF) [5, 6, 7, 8, 9]. Although proposing different ML techniques, these works are based on active RF sensing systems. Recently, significant research efforts have been carried out for detecting and classifying human activities (e.g., gestures, postures, gait, etc.) adopting passive RF sensing and DL/ML techniques. The work in [10] proposed a human body movement sensing system, operating in real time and focusing on detecting falling movement and locating more than one human body in the same area. The system exploits RF signals sent by radio devices that operate in the 2.4 GHz ISM band and detects events related to safety concerns by keeping track of radio signal strength indicator (RSSI) footprints. In [11], the influence of human movements on channel state information (CSI) was studied. Many experiments were carried out in order to extract features to detect human posture in complex scenarios, and commercial Wi-Fi devices were used to perform experiments that reached a detection rate of approximately 90%. An indoor motion recognition and classification method was proposed in [12] adopting a reference and a surveillance channel. Adaptive filtering was performedin order to eliminate the echoes from the surveillance signals. Then, a DL method was used to classify the time series signal into a certain motion, achieving an accuracy rate of approximately 70% in real-time operation. CSI was also considered in [13] to detect human presence using an RF phase and magnitude information to train DL models. In [14], a study was conducted on both passive and active RF sensing systems. Five different activities were distinguished, as well as their location within three indoor environments. In [15], a device-free system for fall detection was proposed, and CSI was exploited as the input to identify the activities in three different indoor scenarios using devices equipped with 802.11n cards. Our work is motivated by the opportunities offered by the existing Frequency-Modulated (FM) radio broadcasting systems to detect indoor human motion through the analysis of the FM signal received by multiple devices. More specifically, our focus is on the development and testing of a passive RF testbed operating in FM radio bands, which allows experimentation with innovative human posture classification techniques. The contributions of this work include the following:  
* The design of a testbed to perform passive RF sensing in FM radio bands. The testbed is prepared to not only gather RF information to be used in an offline manner but also to process the RF information in real time, allowing experimentation with innovative classification techniques;
* The specification of human posture scenarios that were used to sample RF information to be characterized in an offline manner to assist the development of the real-time sensing methodology;
* A general assessment of the importance of the multiple features computed from the received FM signals through the comparison of its score;
* A dynamic selection of the best features to adopt in the classification process according to the features' score of the data gathered during an initial calibration scheme;
* The assessment of three classification techniques showing that simple classification schemes can easily achieve approximately 90% classification accuracy.