Characterization of the cough monitoring device for TB patients based on the MAX9814 sound sensor

Mycobacterium tuberculosis is the cause of tuberculosis (TBC), one of the deadliest diseases in the world that affects the respiratory system. One of the nations having the highest number of tuberculosis cases worldwide is Indonesia. Health professionals must improve patient monitoring as one way to address this issue. This work aims to compile and describe monitoring instruments. The monitoring sensor is intended to help medical professionals treat patients and raise the quality of life for TB patients. Using advancements in technology, specifically the Internet of Things (IoT) to remotely operate electronic equipment, the Arduino Cloud Web serves as a platform for transmitting and storing patient cough data, enabling medical professionals to Recognize the intensity of the cough at any moment and act accordingly. Data on the sensor's accuracy and error values, sensitivity, repeatability, precision, and resolution are gathered as part of this monitoring sensor characterisation process. The instrument was calibrated using an Audiosensor with a 1000Hz audio generator and an SLM (Sound Level Meter) calibrator prior to data collection. The investigation yielded excellent results, with an accuracy rating of 96,14% and an error of 3,86%. This figure is reasonably close to the estimated value of 5% that has been calculated for the Gaussian distribution. The SLM with a sensor has an average value of 0.05, whereas the audiotool has a sensor value of 0.02. 50% is the repeatability value, 0.01% is the precision value, and 0.0125 is the sensor resolution.