The starting point of this graduation project is noises that nurses experience in the ICU (Intensive Care Unit). There is a defined medical symptom which is called “alarm fatigue” that refers to numb auditory senses, stress, low job satisfaction as a result of being exposed to excessive noise for the extended period, and it often leads to low job performances in the end. One report reveals that 65% of significant ICU incidents occur by not responding to alarms appropriately (Cho, Kim, Lee & Cho, 2016). Since the alarm is not a single source of noises in the ICU, this report first defines which sound group will be seen as a culprit of “noise fatigue” as an expanded concept of alarm fatigue in this project. The way this graduation deals with noise in the ICU is different from the conventional approach of providing a design intervention as a solution. In this project, the focus is rather how sound stimuli and stress level of nurses can be precisely captured from the perspective of nurses and be correlated altogether so that the new system can function as an investigation tool for further design intervention to improve the sound environment in the ICU. In this regard, the sound classification method using machine learning, heart rate tracking as a means of an objective stress level assessment, and emotion report as a subjective stress evaluation tool was studied. Furthermore, beacons, an indoor positioning detection device, was investigated and combined with the project concept, so that Cacophony Mapper can function as a mobile data collection platform with dynamic data summary and analysis. After the exploration of possible technology options for this project, the way of combining opted technologies was contemplated. As a result, two devices for heart rate tracking and sound collection, one mobile application for emotion report and data analysis, and a web platform which function as a hub for the data summary were developed as Cacophony Mapper prototype. In the evaluation phase, the reliability of the sound filter was tested by directly putting the WAV sound data and a dataset collected through a user test, separately. Two datasets were compared to find the significant impact of the environmental factors so that the external impact can be minimized by further optimization. Also, the usability of the overall system and the application was evaluated. It has been done to see the possibility of implementing the Cacophony Mapper system in a medical environment and find which aspect of usability should be improved for further design development.