A Passive RF Testbed for Human Posture Classification in FM Radio Bands

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This paper explores the opportunities and challenges for classifying human posture in indoor scenarios by analyzing the Frequency-Modulated (FM) radio broadcasting signal received at multiple locations. More specifically, we present a passive RF testbed operating in FM radio bands, which allows experimentation with innovative human posture classification techniques. After introducing the details of the proposed testbed, we describe a simple methodology to detect and classify human posture. The methodology includes a detailed study of feature engineering and the assumption of three traditional classification techniques. The implementation of the proposed methodology in software-defined radio devices allows an evaluation of the testbed’s capability to classify human posture in real time. The evaluation results presented in this paper confirm that the accuracy of the classification can be approximately 90%, showing the effectiveness of the proposed testbed and its potential to support the development of future innovative classification techniques by only sensing FM bands in a passive mode.
Original languageEnglish
Article number9563
Number of pages17
Issue number23
Publication statusPublished - 1 Dec 2023


  • context awareness
  • human posture classification
  • machine learning
  • performance evaluation
  • RF passive sensing


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