# Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment
###### Abstract  
This research presents the usage of modern 5G C-Band sensing for health care monitoring. The focus of this research is to monitor the respiratory symptoms for COPD (Chronic Obstructive Pulmonary Disease). The C-Band sensing is used to detect the respiratory conditions, including normal, abnormal breathing and coughing of a COPD patient by utilizing the simple wireless devices, including a desktop system, network interface card, and the specified tool for the extraction of wireless channel information with Omni directional antenna operating at 4.8 GHz frequency. The 5G sensing technique enhances the sensing performance for the health care sector by monitoring the amplitude information for different respiratory activities of a patient using the above-mentioned devices. This method examines the rhythmic breathing patterns obtained from C-Band sensing and digital respiratory sensor and compared the result. C-Band; Chronic Obstructive Pulmonary Disease (COPD); wireless channel information +
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include wheezing, caused by the opening of small airways, hypoxemia (low oxygen level in the blood), hypercapnia (high level of carbon dioxide in the blood), Dyspnea, chronic cough and sputum production and chest tightness. The main cause of COPD is smoking. Other causes include the exposure to different lung irritants, including dust, air pollution etc. The morbidity rate increases in COPD patients at the age of 40 [4; 5]. Worldwide, it is the 4 th leading cause of death and it is increasing nowadays. In COPD, maintaining the breathing rate is very important for good health. Respiratory activity is an important parameter for measuring the health of any person [6]. COPD is a progressive disease and with the passage of time without any early detection or diagnosis, it leads to death. Early detection of the disease by careful monitoring of the symptoms reduces the severity of the disease. In COPD, the breathing pattern of a patient becomes worse with the passage of time so there must be some procedure or technique to measure the breathing pattern for diagnosis. The most common breathing monitoring system is invasive monitoring, which requires physical contact with the patient's body. Non-invasive breathing monitoring is becoming more popular in the health sector by overcoming the drawbacks of traditional invasive systems. Radio frequency techniques is the leading method due to the effective monitoring of very sensitive and small breathing patterns. There are many other techniques for this like Doppler radar, which is used to measure the shift in the signal reflected from the human body [7]. [PERSON] et al. [6], detect the breathing by using IR sensors and Accelerometer signals. In [8] [PERSON] et al. measure the breathing pattern using passive radar system, which is also a non-contact breathing monitoring system. In [9], [PERSON] et al. show the usage of Ubi-Breathe for the harnesses of the (received signal strength indicator) RSSI on WiFi devices. Another approach which proposed the idea of using a (channel state information) CSI-based breathing monitoring system is [10] by [PERSON] et al. This proposes the technique for respiratory monitoring and utilizes periodogram for spectral analysis due to this method requiring longer duration for completing the task. In our work we propose a non-invasive technique to measure the breathing patterns and coughing of COPD patients. This system can monitor the patient in a timely manner and can carefully detect breathing activity and coughing. Our system leverages the usage of wireless devices operating at 4.8 GHz frequency, Omni directional antenna connected with RF signal generator and the network interface card (NIC) for the 5G wireless technology. The breathing activity and coughing induces different imprints for wireless channel information (WCI), which helped in the monitoring of breathing patterns of patients. We also used an invasive sensor for monitoring breathing and compared the results with WCI data. The organization of the paper is as follows.