Advancing Stress Detection with Machine Learning: A Study on Multimodal Data Integration | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Reshu Gupta: School of Electronics, Electrical & Mechanical Engineering (SEE & ME) Shobhit Institute of Engineering & Technology (Deemed to be University) NH-58,Modipuram,Meerut,Uttar Pradesh,India

2. Aniket Kumar: School of Electronics, Electrical & Mechanical Engineering (SEE & ME) Shobhit Institute of Engineering & Technology (Deemed to be University) NH-58,Modipuram,Meerut,Uttar Pradesh,India

Abstract:

Stress detection is a critical area in mental health, impacting both individual well-being and productivity. Traditional methods of stress assessment are often subjective and time-consuming. Recent advancements in machine learning have opened new avenues for automatic stress detection using multimodal data fusion techniques, which integrate diverse data sources such as physiological signals, behavioral cues, and contextual information. This paper explores the state-of-the-art multimodal data fusion techniques for automatic stress detection, presenting a comprehensive literature review, detailed methodology, and an analysis of their efficacy. The study concludes by discussing the challenges, potential solutions, and future directions in the field.

Page(s): 174-177

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 3, Year: 2024

Keywords:

machine learning , Mental health , Automatic stress detection , behavioral analysis , multimodal data fusion , physiological signals

References:

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