Authors: Prajakta Salve, Abhay More, M. Rayyan Ansari, Rushikesh Kande4
Abstract: Stress is a state that a lot of people experience while working on various activities at school or at work. As far as I noticed, stress begins in the brain. Moreover, stress manifests itself physically in some cases. Heart rate, heart-rate variability, electrical skin response, respiration, and blood flow are just some examples of body reactions to stress. Such manifestations may help to create stress detection technologies. However, only one body sign cannot serve as the only basis for stress assessment since movement, sitting position, temperature of the body, disease, medication, sensor placement and more may influence results. In my attempt to integrate the body monitoring with meditation, I would like to focus on relaxation through the use of technology. At the moment, I have the prototype of the system that operates using the benchtop. The system uses an ESP32, optical sensor, and an SH1106 OLED display. The optical sensor captures data related to the light. This data is processed using the MAXIM algorithm to calculate heart rate and blood oxygen levels (SpO₂). When the readings are valid the screen shows heart rate, SpO₂ and a rough stress level. This stress level is based on fixed thresholds. It is not an assessment.I have also started using the WESAD dataset. This dataset contains stress and emotional data from people. One subject’s file has been loaded into Python. The chest and wrist signals were checked. A short ECG segment was plotted. This step helps lay the groundwork for feature extraction and machine-learning model building. Now the system is still in stages. A machine-learning model has not been trained. A mobile app made with Flutter is not ready. Automated analysis before and, after meditation is not yet set up. The stress label used today is rule-based. It should not be treated as a measure of stress. This report goes over why the project was started what others have done in the field the hardware setup, how the software works what was done with the data, where things stand now what the limits are and what steps come next. The prototype is meant for teaching and wellness research. It is not meant for diagnosis. The prototype is the embodiment of the easy-to-use and non-invasive method of heart rate and SpO2 measurement, and stress estimation using some pre-defined rules. The application of machine learning methods for further stress classification was also considered within this study. Even though the suggested prototype is designed only for initial monitoring and education purposes, not for clinical diagnostics, it opens up possibilities for development of cheap technological solutions for stress monitoring and mental wellness.
DOI: https://doi.org/10.5281/zenodo.23977133
