A Wireless Respiratory Monitoring System Using a Wearable Patch Sensor Network

Wireless body sensors are increasingly used by clinicians and researchers in a wide range of applications, such as sports, space engineering, and medicine. Monitoring vital signs in real time can dramatically increase diagnosis accuracy and enable automatic curing procedures, e.g., detect and stop epilepsy or narcolepsy seizures. Breathing parameters are critical in oxygen therapy, hospital, and ambulatory monitoring, while the assessment of cough severity is essential when dealing with several diseases, such as chronic obstructive pulmonary disease. In this paper, a low-power wireless respiratory monitoring system with cough detection is proposed to measure the breathing rate and the frequency of coughing. This system uses wearable wireless multimodal patch sensors, designed using off-the-shelf components. These wearable sensors use a low-power nine-axis inertial measurement unit to quantify the respiratory movement and a MEMs microphone to record audio signals. Data processing and fusion algorithms are used to calculate the respiratory frequency and the coughing events. The architecture of each wireless patch-sensor is presented. In fact, the results show that the small $26.67 \times 65.53$ mm2 patch-sensor consumes around 12–16.2 mA and can last at least 6 h with a miniature 100-mA lithium ion battery. The data processing algorithms, the acquisition, and wireless communication units are described. The proposed network performance is presented for experimental tests with a freely behaving user in parallel with the gold standard respiratory inductance plethysmography.

[1]  Minoru Sasaki,et al.  A Wearable Capacitive Sensor for Monitoring Human Respiratory Rate , 2013 .

[2]  Won-Ki Kim,et al.  Improvement of Dynamic Respiration Monitoring Through Sensor Fusion of Accelerometer and Gyro-sensor , 2014 .

[3]  Mohammad Nurul Huda,et al.  Cough detection using speech analysis , 2015, 2015 18th International Conference on Computer and Information Technology (ICCIT).

[4]  Young-Joo Suh,et al.  Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[5]  Bessam Abdulrazak,et al.  Nonintrusive Vital Signs Monitoring for Sleep Apnea Patients: A Preliminary Study , 2018, IEEE Access.

[6]  Y. Ono,et al.  Piezoelectric membrane sensor and technique for breathing monitoring , 2008, 2008 IEEE Ultrasonics Symposium.

[7]  Emiliano Schena,et al.  Respiratory and cardiac rates monitoring during MR examination by a sensorized smart textile , 2017, 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[8]  Jian Yang,et al.  Detection of breathing and heartbeat by using a simple UWB radar system , 2014, The 8th European Conference on Antennas and Propagation (EuCAP 2014).

[9]  S. Schappert,et al.  Ambulatory care visits to physician offices, hospital outpatient departments, and emergency departments: United States, 1997. , 1997, Vital and health statistics. Series 13, Data from the National Health Survey.

[10]  Thierry Dutoit,et al.  Objective Study of Sensor Relevance for Automatic Cough Detection , 2013, IEEE Journal of Biomedical and Health Informatics.

[11]  C. Burt,et al.  Ambulatory care visits to physician offices, hospital outpatient departments, and emergency departments: United States, 2001-02. , 2006, Vital and health statistics. Series 13, Data from the National Health Survey.

[12]  P. Sly,et al.  Laser monitoring of chest wall displacement. , 1997, The European respiratory journal.

[13]  Madhuka Jayawardhana,et al.  Enhanced detection of sleep apnoea using heart-rate, respiration effort and oxygen saturation derived from a photoplethysmography sensor , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

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

[15]  Benoit Gosselin,et al.  Wireless respiratory monitoring and coughing detection using a wearable patch sensor network , 2017, 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS).

[16]  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.

[17]  Ki H. Chon,et al.  Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset , 2016, IEEE Journal of Biomedical and Health Informatics.

[18]  Yuko Ohno,et al.  Development of noncontact respiration monitoring method with web-camera during sleep , 2017, 2017 IEEE 6th Global Conference on Consumer Electronics (GCCE).

[19]  Olga Boric-Lubecke,et al.  Non-contact displacement estimation using Doppler radar , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Robert E. Kearney,et al.  A contact-free respiration monitor for smart bed and ambulatory monitoring applications , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[21]  Partha Sarati Das,et al.  A Flexible Capacitive Pressure Sensor for Wearable Respiration Monitoring System , 2017, IEEE Sensors Journal.

[22]  Junji Satake,et al.  Development of a Breathing Detection Robot for a Monitoring System , 2016, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS).

[23]  D. K. Arvind,et al.  Respiratory Rate and Flow Waveform Estimation from Tri-axial Accelerometer Data , 2010, 2010 International Conference on Body Sensor Networks.

[24]  Ahmet Öncü.  A 24-GHz Doppler sensor system for cardiorespiratory monitoring , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.