A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Khan, Muhammad Bilal; Zhang, Zhiya; Li, Lin; Zhao, Wei; Hababi, Mohammed Ali Mohammed Al; Yang, Xiaodong; Abbasi, Qammer H.

doi:10.3390/mi11100912

Cited by 26 publications

(16 citation statements)

References 87 publications

(156 reference statements)

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“…To overcome the limitations of wearable sensing technology, non-contact healthcare has been proposed by researchers [ [64] , [65] , [66] ]. As the name suggests, non-contact technology requires no direct contact with the body to monitor the patient status.…”

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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“…Here, the OFDM system uses 256 subcarriers for data transmission on a 20 MHz channel. The channel frequency response for all subcarriers can be represented by Equation (2) as:…”

Section: Data Extraction Layermentioning

confidence: 99%

“…There are various ways to diagnose COVID-19 infection, of which monitoring breathing rate (BR) is considered one of the most significant indications of COVID-19. Therefore, investigating the BR and its connection with COVID-19 symptoms is now a popular area of research [2]. BR is usually defined as the number of breaths an individual takes per minute when resting.…”

Section: Introductionmentioning

confidence: 99%

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

Rehman

Shah

Khan

et al. 2021

Sensors

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The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as coronavirus disease (COVID)-19, has appeared as a global pandemic with a high mortality rate. The main complication of COVID-19 is rapid respirational deterioration, which may cause life-threatening pneumonia conditions. Global healthcare systems are currently facing a scarcity of resources to assist critical patients simultaneously. Indeed, non-critical patients are mostly advised to self-isolate or quarantine themselves at home. However, there are limited healthcare services available during self-isolation at home. According to research, nearly 20–30% of COVID patients require hospitalization, while almost 5–12% of patients may require intensive care due to severe health conditions. This pandemic requires global healthcare systems that are intelligent, secure, and reliable. Tremendous efforts have been made already to develop non-contact sensing technologies for the diagnosis of COVID-19. The most significant early indication of COVID-19 is rapid and abnormal breathing. In this research work, RF-based technology is used to collect real-time breathing abnormalities data. Subsequently, based on this data, a large dataset of simulated breathing abnormalities is generated using the curve fitting technique for developing a machine learning (ML) classification model. The advantages of generating simulated breathing abnormalities data are two-fold; it will help counter the daunting and time-consuming task of real-time data collection and improve the ML model accuracy. Several ML algorithms are exploited to classify eight breathing abnormalities: eupnea, bradypnea, tachypnea, Biot, sighing, Kussmaul, Cheyne–Stokes, and central sleep apnea (CSA). The performance of ML algorithms is evaluated based on accuracy, prediction speed, and training time for real-time breathing data and simulated breathing data. The results show that the proposed platform for real-time data classifies breathing patterns with a maximum accuracy of 97.5%, whereas by introducing simulated breathing data, the accuracy increases up to 99.3%. This work has a notable medical impact, as the introduced method mitigates the challenge of data collection to build a realistic model of a large dataset during the pandemic.

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“…Moreover, they conducted a time-frequency analysis to accumulate the Doppler spectral energy. Khan et al [30] conducted a systematic review of noncontact sensing related to COVID-19. The main idea was to suggest a solution for the early diagnosis of this disease based on common early symptoms such as coughing and shortness of breath.…”

Section: Related Workmentioning

confidence: 99%

Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

et al. 2021

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The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.

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A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19

Cited by 26 publications

References 87 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

Improving Machine Learning Classification Accuracy for Breathing Abnormalities by Enhancing Dataset

Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

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