Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment

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.

[1]  A. Enis Çetin,et al.  Breathing detection based on the topological features of IR sensor and accelerometer signals , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[2]  Andrew Hunter,et al.  Unconstrained Video Monitoring of Breathing Behavior and Application to Diagnosis of Sleep Apnea , 2014, IEEE Transactions on Biomedical Engineering.

[3]  D. Flandre,et al.  Miniaturized Wireless Sensing System for Real-Time Breath Activity Recording , 2010, IEEE Sensors Journal.

[4]  Ying Wang,et al.  5G Spectrum: is china ready? , 2015, IEEE Communications Magazine.

[5]  Syed Aziz Shah,et al.  Breathing Rhythm Analysis in Body Centric Networks , 2018, IEEE Access.

[6]  Jie Tian,et al.  Wandering Pattern Sensing at S-Band , 2018, IEEE Journal of Biomedical and Health Informatics.

[7]  O. Boric-Lubecke,et al.  Single-channel receiver limitations in Doppler radar measurements of periodic motion , 2006, 2006 IEEE Radio and Wireless Symposium.

[8]  P.H. Quanjer,et al.  Portable electronic peak flowmeter for improved diagnosis of chest diseases in COPD patients , 1993, 1993 IEEE Instrumentation and Measurement Technology Conference.

[9]  K. J. Ray Liu,et al.  TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection , 2018, IEEE Transactions on Biomedical Engineering.

[10]  Masood Ur Rehman,et al.  Internet of Things for Sensing: A Case Study in the Healthcare System , 2018 .

[11]  Thierry Troosters,et al.  Physical inactivity in patients with COPD, a controlled multi-center pilot-study. , 2010, Respiratory medicine.

[12]  Y. Karplyuk,et al.  Detection of COPD's diagnostic signs based on polyspectral lung sounds analysis of respiratory phases , 2015, 2015 IEEE 35th International Conference on Electronics and Nanotechnology (ELNANO).

[13]  Sneha Kumar Kasera,et al.  Monitoring Breathing via Signal Strength in Wireless Networks , 2011, IEEE Transactions on Mobile Computing.

[14]  Ossi Kaltiokallio,et al.  Non-invasive respiration rate monitoring using a single COTS TX-RX pair , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[15]  Robert J. Piechocki,et al.  Non-contact breathing detection using passive radar , 2016, 2016 IEEE International Conference on Communications (ICC).

[16]  Edward J. Powers,et al.  Experimental measurement of three‐wave coupling and energy cascading , 1989 .

[17]  Yang Hao,et al.  Buried Object Sensing Considering Curved Pipeline , 2017, IEEE Antennas and Wireless Propagation Letters.

[18]  Sima Noghanian,et al.  Antenna Effects on Respiratory Rate Measurement Using a UWB Radar System , 2018, IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology.

[19]  J. Garcia-Aymerich,et al.  Risk factors of readmission to hospital for a COPD exacerbation: a prospective study , 2003, Thorax.

[20]  M. Abramowitz,et al.  Handbook of Mathematical Functions, with Formulas, Graphs, and Mathematical Tables , 1966 .

[21]  Xu Chen,et al.  Tracking Vital Signs During Sleep Leveraging Off-the-shelf WiFi , 2015, MobiHoc.

[22]  Khaled A. Harras,et al.  UbiBreathe: A Ubiquitous non-Invasive WiFi-based Breathing Estimator , 2015, MobiHoc.