Assessed the potential of electro dermal activity (EDA) sensors in accurately capturing and classifying user distress levels.
- Formulated experimental paradigms ('Easy', 'Hard', and 'Pause') based on consultations with psychotherapists to simulate varying levels of stress.
- Collected EDA data from users during these paradigms to capture physiological responses associated with different stress levels.
- Pre-processed the collected data to remove noise and standardize measurements, ensuring clean and reliable inputs for analysis.
- Implemented a classification algorithm to create a predictive model that classifies users as stressed or not stressed based on their EDA data.
- Conducted validation and performance evaluation of the model using standard metrics to ensure its accuracy and reliability.
- Usage of non-invasive EDA sensors as a tool for real-time stress detection proved to be successful in detecting stress.
- This work provides an objective measure of stress levels helping psychotherapists and researchers better understand and manage stress-related conditions.
- It has potential for development as wearable health devices, offering potential benefits in personal health management and workplace wellness programs.