Workplace stress assessment using emotional recognition and heart rate techniques - Vega, Alejandro (2025)
This study aims to develop a mechanism to evaluate employee health through emotion recognition (ER) analysis and heart rate measurement, with the goal of establishing correlations with stress levels. It is the second phase of a previous study, where current results will be compared to those previously obtained. In both studies, biometric devices such as cameras were used to capture facial images analyzed with artificial intelligence, and sensors in mobile phones or smartwatches to record heart rate. Given the challenge of emotion detection, we propase using the DeepFace algorithm for facial emotion recognition, which has demonstrated a 94% accuracy. Additionally, employees will complete a self-administered questionnaire about their emotional and mental state (neutral, tired, energized), allowing for a comparison between detected emotions and subjective reports. This will provide a better understanding of the accuracy of emotion recognition and contribute to improving health status evaluation.