Flexible and Scalable Software Defined Radio Based Testbed for Large Scale Body Movement

Abstract

Human activity (HA) sensing is becoming one of the key component in future healthcare system. The prevailing detection techniques for IHA uses ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concerns as well. This paper proposes a novel, non-invasive, easily-deployable, flexible and scalable test-bed for identifying large-scale body movements based on Software Defined Radios (SDRs). Two Universal Software Radio Peripheral (USRP) models, working as SDR based transceivers, are used to extract the Channel State Information (CSI) from continuous stream of multiple frequency subcarriers. The variances of amplitude information obtained from CSI data stream are used to infer daily life activities. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis and Naïve Bayes are used to evaluate the overall performance of the test-bed. The training, validation and testing processes are performed by considering the time-domain statistical features obtained from CSI data. The K-nearest neighbour outperformed all aforementioned classifiers, providing an accuracy of 89.73%. This preliminary non-invasive work will open a new direction for design of scalable framework for future healthcare systems.

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“…However, in all cases, there is a high cost to establish a specific testbed. In [30], a system based on SDR using USRP was proposed. The experiment simulated an FMCW system to analyze the phase change status caused by respiration.…”

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“…Software-defined radio-based Testbed for large-scale body movement using KNN-based machine learning classification with an accuracy of up to 90% is proposed by Ashleibta et al (2020). This novel non-invasive approach will pave the way for the development of a sustainable framework for future healthcare systems.…”

“…After obtaining the proposed result, it was fed into machine learning algorithms and KNN was found to be the best model with the highest accuracy of 91%. Ashleibta et al (2020) proposed human activity monitoring, namely, exercise, picking up an object, sitting down, standing up, and other activities as shown in Figure 15. In this experiment, using two universal software radio peripheral (USRP) models that function as SDR-based transceivers, the fluctuation of channel state information amplitude was retrieved from a continuous stream of various frequency sub-carriers.…”

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“…WiFi sensing essentially exploits the received signal strength (amplitude information) indicators and channel state information (amplitude and phase information), which can be used in a large number of applications, specifically for detecting the activities of daily living and tiny chest and cardiac movements with varying frequencies. The RSSI data are the averaged-out signal received by a commercial network interface card, while the CSI data describe the overarching physical wireless medium in terms of various frequency channels [8,38]. The RSSI data are highly susceptible to random noise and unstable each time the data are received.…”