Non-Invasive RF Sensing for Detecting Breathing Abnormalities Using Software Defined Radios

Ashleibta, Aboajeila Milad; Abbasi, Qammer H.; Shah, Syed Aziz; Ali, Najah Abu; Imran, Muhammad Ali

doi:10.1109/jsen.2020.3035960

Cited by 27 publications

(14 citation statements)

References 23 publications

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“…All major non-contact techniques that can be effectively used for the detection and monitoring of COVID-19 symptoms are described in this paper. Moreover, Table 1 [ [81] , [82] , [83] , [84] , [85] , [86] , [87] , [88] , [89] , [90] , [91] , [92] , [93] ] summarizes studies in which different non-contact sensing technologies are effectively used to monitor an abnormal breathing rate, which is the primary sign of COVID-19. Fig.…”

Section: Intelligent Healthcare Technologymentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), which caused the COVID-19 pandemic, has affected more than 250 million people worldwide. With the recent rise of a new Delta variant, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this review paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, RADAR, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.

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Section: Intelligent Healthcare Technologymentioning

confidence: 99%

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Saeed

Shah

Ahmad

et al. 2022

Journal of Pharmaceutical Analysis

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“…Using specialised circuit hardware, Allsee technologies introduced a short-range detection technique for gesture recognition that is less than 2.5 ft [ 36 , 37 ]. The sole method for extracting wireless CSI from WiFi signals without upgrading or modifying the hardware is to employ a platform based on SDR [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

Saeed

Shah

et al. 2022

Sensors

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Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal’s Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking.

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“…A total of 225 events involving 45 FOG cases are taken from 15 patients using 30 sub-carriers, and the classification of a deep neural network with high accuracy of more than 90% was achieved (Tahir et al, 2019). Ashleibta et al (2021a) proposed non-contact breathing detection and…”

Section: Figure 10mentioning

confidence: 99%

Radio frequency sensing and its innovative applications in diverse sectors: A comprehensive study

et al. 2022

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Recent advancements in radio frequency (RF) sensing technology can be attributed to the development of the Internet of Things (IoT), healthcare, RF-identification, and communication applications. RF sensing is a multidisciplinary research field that requires expertise in computing, electronics, and electromagnetics to cover all system features, including protocol development, antenna design, sensor integration, algorithm formulation, interconnection, data, and analytics. The overarching aim of this work is to present detailed information about RF technologies and their innovations and application diversity from the novel work carried out at CSI Lab 1, together in one platform with an extensive survey. This study presents state-of-the art applications and RF sensing that include W-Fi, radar, and SDR and RFID-based sensing. A comprehensive survey and study of the advantages and limitations of each non-contact technology is discussed. Additionally, open research gaps have been identified as well. Decades of knowledge and experience have been put to use to meet new challenges and demands. The development and study of RF systems, IoT, RFID sensing, and research and deployment activities, are briefly discussed. The emerging research projects with industry, institutional research centers, and academic studies are also addressed. Finally, an outline of identified potential future research areas is provided, emphasizing opportunities and challenges.

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Non-Invasive RF Sensing for Detecting Breathing Abnormalities Using Software Defined Radios

Cited by 27 publications

References 23 publications

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review

Multiple Participants’ Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing

Radio frequency sensing and its innovative applications in diverse sectors: A comprehensive study

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