A new physiological signal acquisition patch designed with advanced respiration monitoring algorithm based on 3-axis accelerator and gyroscope

In a gradually aging society, families and hospitals have a growing demand for reliable and unobtrusive physiological signal monitoring for elderly people. However, the existing respiration rate monitoring methods and algorithms are still unsatisfactory. In this work, we introduce a physiological signal acquisition patch which integrates 3-axis accelerator and 3-axis gyroscope to estimate respiration rate, as well as ECG(electrocardiogram) sensor and surface temperature sensor. A complete set of respiration rate estimation algorithms is embedded in our patch, which can be used to identify whether the patch is worn or not, and to recognize, segment, de-noise and reconstruct the respiration signal. In-situ experiments have been conducted to prove the validity of the algorithms described in this paper and the possibilities of estimating respiration rate using a physiological signal acquisition patch. The mean absolute error (MAE) is 0.11(about ±0.7 times in a minute), which is the least among similar studies that acquire respiratory rate from 3-axis accelerators or electrocardiogram.

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

[2]  Chunlong Tu,et al.  A low power miniaturized monitoring system of six human physiological parameters based on wearable body sensor network , 2015 .

[3]  Ming Liu,et al.  A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts , 2017, Science China Information Sciences.

[4]  James D. Louck,et al.  Angular Momentum in Quantum Physics: Theory and Application , 1984 .

[5]  Ming Liu,et al.  Advanced EMD method using variance characterization for PPG with motion artifact , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[6]  Ronald M. Aarts,et al.  Performance evaluation of a tri-axial accelerometry-based respiration monitoring for ambient assisted living , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[8]  S.H. Nam,et al.  The Preliminary Study of Unobtrusive respiratory monitoring for e-health , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[9]  Robert B. McGhee,et al.  An extended Kalman filter for quaternion-based orientation estimation using MARG sensors , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[10]  Bang-Yu Huang,et al.  A wearable respiration monitoring system based on digital respiratory inductive plethysmography , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  James T. Patrie,et al.  Development and Preliminary Validation of Heart Rate and Breathing Rate Detection Using a Passive, Ballistocardiography-Based Sleep Monitoring System , 2009, IEEE Transactions on Information Technology in Biomedicine.

[12]  François Portet,et al.  A real time qrs detection algorithm pdf , 2015 .

[13]  Tsair Kao,et al.  Respiratory Rate Estimation by Using ECG, Impedance, and Motion Sensing in Smart Clothing , 2017, Journal of Medical and Biological Engineering.

[14]  Dan Wu,et al.  Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals , 2013 .

[15]  Junyou Huang.  Study of Autoregressive (AR) Spectrum Estimation Algorithm for Vibration Signals of Industrial Steam Turbines , 2014 .

[16]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.