VOLUME 05 ISSUE 08 AUGUST 2026
1Aakanksh Chandra, 2Meenu Chopra, 3Cosmena Mahapatra
1,2,3School of information technology Vivekananda Institute of Professional Studies-Technical Campus, AU-Block (Outer Ring Road), Pitampura, Delhi – 110034, India
DOI : https://doi.org/10.58806/ijsshmr.2026.v5i8n16Google Scholar Download Pdf
ABSTRACT
Employee burnout has emerged as a critical occupational health concern in digitally intensive workplaces, particularly in high-demand sectors such as the Information Technology (IT) industry [1][3]. This study proposes a privacy-preserving multimodal Artificial Intelligence framework for early detection of burnout-related emotional exhaustion using aggregated behavioural and linguistic indicators [7] [11]. Drawing on the multidimensional burnout model, the system integrates sentiment dynamics, workload variability, and interaction-pattern features while avoiding personally identifiable data [5] [11]. Empirical evaluation demonstrates that multimodal mood scores significantly predict emotional exhaustion (R² = 0.48, p < .01), showing an 18 % improvement in predictive sensitivity compared with single-modality approaches [7]. The report outlines the importance of ethical governance, privacy, and transparency in AI-based monitoring [11]. The outcomes show that privacy-first multimodal monitoring can facilitate the early identification of a variety of problems which helps in improving employee privacy and trust [9] [10] [11].
KEYWORDS:AI in workplace, Burnout detection, Maslach Burnout Inventory, Privacy-first AI, Indian IT sector, Multimodal analytics, Occupational mental health
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Cite this Article: Chandra, A., Chopra, M., & Mahapatra, C. (2026). Evaluating the Effectiveness of a Privacy-First AI-Based Multimodal Mood Monitoring System (Sanrakshak) among Office Employees. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCHS, 5(8), 2243-2252. https://doi.org/10.58806/ijsshmr.2026.v5i8n16