Tidal volume variability and respiration rate estimation using a wearable accelerometer sensor
The measurement of respiration rate and tidal volume variability are critical to the diagnosis and monitoring of a wide range of breath disorders as well as being useful broader parameters of a patient's condition. This paper presents a portable real time platform designed to support a computationally efficient human respiratory tracking system for medical applications. The proposed system is designed particularly for patients with breathing problems (e.g. respiratory complications after surgery) or sleep disorders. We introduce the use of accelerometer sensor to detect changes in the anterior posterior diameter of the chest; whereas these changes provide an accurate measurement of respiration rate as well as tidal volume variability. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud database. The experiments are conducted with 8 subjects and the overall error in respiration rate calculation is obtained 0.2% considering SPR BTA spirometer as the reference. We also present a method for Tidal Volume variability (TVvar) estimation while validated using Pearson correlation. The mean value of the correlation coefficient between TVvar derived from the accelerometer and spirometer for all subjects and three breath patterns is 0.87 which shows a high correspondence of two signals. Furthermore, the results indicate that the accelerometer driven TVvar achieves the average MSE 1.6E 03+ 3.69E 03 compared to the reference.