USING AFFECTIVE MONITORING TOOLS TO SUPPORT HR DECISIONS IN EMOTIONALLY DEMANDING FIELD TEAMS

Abstract

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.