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The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.
Muhammad Khan; Mubashir Rehman; Ali Mustafa; Raza Shah; Xiaodong Yang. Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology. Electronics 2021, 10, 1558 .
AMA StyleMuhammad Khan, Mubashir Rehman, Ali Mustafa, Raza Shah, Xiaodong Yang. Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology. Electronics. 2021; 10 (13):1558.
Chicago/Turabian StyleMuhammad Khan; Mubashir Rehman; Ali Mustafa; Raza Shah; Xiaodong Yang. 2021. "Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology." Electronics 10, no. 13: 1558.
Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.
Mubashir Rehman; Raza Shah; Muhammad Khan; Najah AbuAli; Syed Shah; Xiaodong Yang; Akram Alomainy; Muhmmad Imran; Qammer Abbasi. RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. Sensors 2021, 21, 3855 .
AMA StyleMubashir Rehman, Raza Shah, Muhammad Khan, Najah AbuAli, Syed Shah, Xiaodong Yang, Akram Alomainy, Muhmmad Imran, Qammer Abbasi. RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. Sensors. 2021; 21 (11):3855.
Chicago/Turabian StyleMubashir Rehman; Raza Shah; Muhammad Khan; Najah AbuAli; Syed Shah; Xiaodong Yang; Akram Alomainy; Muhmmad Imran; Qammer Abbasi. 2021. "RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices." Sensors 21, no. 11: 3855.