SRISHTI SHRIVASTAVA,NAIMISH NANDA,KUNAL JHA
DOI: https://doi.org/Employees working in field roles that require emotional engagement experience chronic stress and emotional fatigue, which affects their well-being and professional performance. This research proposes a comprehensive affective monitoring framework that uses voice, facial expressions, and text sentiment analysis to classify emotional states, thereby aiding human resource (HR) decision-making. The methodology comprises emotion classification, preprocessing, real-time signal acquisition, and HR dashboard integration. Findings from a four-week field deployment showed a significant reduction in high-stress emotional states alongside a positive trend in emotions after HR changes. There was also an enhancement in job satisfaction and a predictive model was provided for emotional health management. The system acceptance was underscored along with ethical considerations of system transparency and data privacy. This approach offers a scalable HR system solution with compassion for emotionally demanding work settings.
