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Automated and semi-automated medical diagnosis systems based on human physiology have gained considerable popularity and significance in recent years. Physiological features possess unique characteristics contributing to the reliability, accuracy, and robustness of these systems. Research has also focused on detecting conventional positive and negative emotions following laboratory-based stimuli. This survey provides a comprehensive overview of mental stress detection systems, covering physiological data collection, the role of machine learning in Emotion Detection and Stress Detection, evaluation measures, challenges, applications, and popular feature selection methods. The paper makes a noteworthy contribution by exploring links between biological features, emotions, and mental stress. Identifying research voids in this field is emphasized, providing direction for forthcoming investigations.
Keywords:
Stress Monitoring System, Healthcare, Wireless Body Area Network (WBAN), Office Environment.
Cite Article:
"Human Stress Detection Based on Physiological Parameters", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.82 - 85, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401015.pdf
Downloads:
000205146
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator