Implementation of Gyro Accelerometer Sensor for Measuring Respiration Rate Based on Inhale and Exhale Using Kalman Filter - Fitriyana Jayanti; Triwiyanto Triwiyanto; Triana Rahmawati; Abdussalam Ali Ahmed (Jurusan Teknik Elektromedik, POLTEKKES KEMENKES Surabaya, Indonesia, 2022)
Respiratory rate is a fundamental vital sign that is sensitive to different pathological conditions including heart, lung, emotional stress, the influence of body temperature and activity fatigue. The respiratory rate in humans is measured by counting the number of breaths for one minute by monitoring and counting the number of times the chest rises and falls during the inhale and exhale process. Various methods for measuring respiratory rate that are commonly used including pneumograph, impedance and capnography are applied in patient monitoring. This study aims to examine and analyze the application of the kalman filter on the output of the gyro accelerometer sensor to increase the results of the detection of respiratory rates using the gyro accelerometer sensor. This study test was carried out using a patient simulator in Surabaya Ministry of Health Polytechnic nursing laboratory. This simulator patient can simulate respiration with a mechanical work system up and down the chest and abdomen, uses an Arduino Nano microcontroller to filter the output of the gyro accelerometer sensor and the results will be compared before and after the filter. The independent variable in this study is the respiration value, while the dependent variable is the sensor output before being filtered. In the relaxed condition of the respondent The most effective use of the kalman filter is found in the parameters R = 10, Q = 0.1 because in the use of these parameters, the value after being filtered has a value that tends to be stable. The highest error value in the application of the gyro accelerometer sensor occurs at sensor position 1 with R = 1 Q = 10 value of 2,6%. This study shows the effect of differences in respiration values before and after using a kalman filter. This study has limited differences in values that are far between the pre filter and after being filtered in several data collections.