Recently, activity recognition and classification (ARC) of human activity opens new research area in the field health care, security, and privacy of human society. Specifically, the promise of device-free activity recognition platform attracts researchers to develop platform to ensure the correct detection of activity recognition. The technologies, such as Wi-Fi, GSM, and radars, do not require installing cameras or wearable sensors for activity monitoring and recognition. Therefore, this device-free technology has gain popularity in health care and safety measurement systems. Traditional ARC systems depend on wearable sensors such as magic rings and vision technology such as a Microsoft Kinect. In the future, researchers are striving to reduce such devices and targeting a promising device-free sensing system. In this paper, a software-defined radio platform was designed for the detection of human activity. The extensive experiments were performed in the laboratory environment by using two Universal Software Radio Peripheral (USRP) to extract the wireless channel state information (WCSI). The 64-Fast Fourier Transform (FFT) point's Orthogonal frequency division multiplexing (OFDM) signal was used to determine the WCSI. The design of the proposed system can be used for multiple applications due to scalability and flexibility of the software-defined hardware.
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