Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{196643,
        author = {SHOHEB SHABBIR MULLA and KOMAL RAJESH PANDEY and DIVYESH ANIL SHINDE and ADNAN DANISH ANSARI and Prof. Sonali Patil},
        title = {Silent Emotional Burnout Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3708-3713},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196643},
        abstract = {Emotional burnout is a silent psychological epidemic that undermines productivity, mental health, and decision-making in digital-heavy environments. Existing detection methods rely on intrusive modalities such as facial expression analysis, speech processing, or self-reported questionnaires, raising privacy and scalability concerns. This paper proposes a Silent Emotional Burnout Detection System leveraging Behavioral Artificial Intelligence (AI) to infer burnout risk from non-intrusive user interaction patterns: typing speed, keystroke intervals, idle time, error rate, and task-switching frequency. The system employs a hybrid machine learning pipeline combining unsupervised techniques (K-Means clustering, Isolation Forest anomaly detection) with supervised classifiers (Support Vector Machine, Random Forest). Evaluated on a dataset of 1,000 simulated users, our approach achieves 92% accuracy and 0.91 F1-score, with SVM outperforming other models consistent with recent findings [1]. The system is privacy-preserving, hardware-free, and suitable for continuous deployment in remote work and e-learning contexts.},
        keywords = {Burnout Detection, Behavioral AI, Keystroke Dynamics, Human-Computer Interaction, Anomaly Detection, Support Vector Machine},
        month = {April},
        }