International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Multimodal Framework for Real-Time Cognitive and Emotional State Monitoring to Enhance Human Performance and Well-Being |
|---|---|
| ABSTRACT | Human fatigue and stress monitoring has become increasingly important with the rise of continuous screen exposure and remote work environments. Prolonged digital interaction often leads to mental fatigue, reduced focus, and health deterioration. This project presents an AI-based multimodal fatigue and stress detection system that integrates webcam-based visual analysis, posture detection, and physiological signal monitoring to evaluate the user’s cognitive and emotional state in real time. The proposed system architecture combines data acquisition, preprocessing, AI-based analysis, and decision-support modules to assess user stress levels and provide adaptive feedback. Machine learning algorithms analyse facial landmarks, eye movements, and heart rate variability (HRV) patterns to classify fatigue levels. The model emphasizes user privacy, real-time adaptability, and continuous learning through feedback integration. This intelligent system aims to improve workplace well-being and optimize performance through proactive fatigue detection and user-centred design. |
| AUTHOR | PROF. SHEGAR S.R., PROF. DR. CHAUDHARI N. J. , GADGE SIDDHESH SUNIL |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311056 |
| pdf/56_AI-Based Multimodal Framework for Real-Time Cognitive and Emotional State Monitoring to Enhance Human Performance and Well-Being.pdf | |
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