Human Sensing via Passive Spectrum Monitoring

Human sensing is significantly improving our lifestyle in many fields such as elderly healthcare and public safety. Research has demonstrated that human activity can alter the passive radio frequency (PRF) spectrum, which represents the passive reception of RF signals in the surrounding environment without actively transmitting a target signal. This paper proposes a novel passive human sensing method that utilizes PRF spectrum alteration as a biometrics modality for human authentication, localization, and activity recognition. The proposed method uses software-defined radio (SDR) technology to acquire the PRF in the frequency band sensitive to human signature. Additionally, the PRF spectrum signatures are classified and regressed by five machine learning (ML) algorithms based on different human sensing tasks. The proposed Sensing Humans among Passive Radio Frequency (SHAPR) method was tested in several environments and scenarios, including a laboratory, a living room, a classroom, and a vehicle, to verify its extensiveness. The experimental results show that the SHAPR method achieved more than 95% accuracy in the four scenarios for the three human sensing tasks, with a localization error of less than 0.8 m. These results indicate that the SHAPR technique can be considered a new human signature modality with high accuracy, robustness, and general applicability

A UHF-RFID gate control system based on a convolutional neural network

In this paper, a robust, easy-to-deploy UHF-RFID system to classify transpallet actions at gates or checkpoints has been presented. The system is based on deploying a set of reference RFID tags on the floor of the checkpoint arranged in a matrix form. In addition, a single RFID reader antenna, which is over the checkpoint, is used to query both the reference tags of the system and those that are used identify goods and transpallets. When a transpallet crosses the controlled gate or checkpoint, it directly blocks the reference tags under it. Hence, reference RFID tags under the transpallet are progressively shadowed. As a consequence, if the number of readings of each reference tag is observed versus time, the movement direction of the transpallet can be inferred. This information was used to build images which are then fed to a Convolutional Neural Network (CNN) that classifies transpallet movements in incoming, outgoing or passing through the controlled checkpoint. A total of 159 measurements were acquired for different transpallet trajectories using 24 reference tags and a CNN was trained showing promising results