TY - JOUR
AU - Rehman, Mubashir
AU - Shah, Raza Ali
AU - Khan, Muhammad Bilal
AU - AbuAli, Najah Abed
AU - Shah, Syed Aziz
AU - Yang, Xiaodong
AU - Alomainy, Akram
AU - Imran, Muhammad Ali
AU - Abbasi, Qammer H.
PY - 2021
DA - June
TI - RF sensing based breathing patterns detection leveraging USRP devices
JO - Sensors
VL - 21
IS - 11
DO - https://doi.org/10.3390/s21113855
AB - 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.
PB - MDPI
UR - http://eprints.gla.ac.uk/243570/
KW - Coronavirus (COVID-19)
KW - Digital health and technology
ER