Employing Multimodal Machine Learning for Stress Detection

In the current information age, the human lifestyle has become more knowledge-oriented, leading to sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected, however crucial, aspects of today’s fast-paced world. Mental health i...

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Autores principales:	Rahee Walambe, Pranav Nayak, Ashmit Bhardwaj, Ketan Kotecha
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/763a764526024a7ca9ce2b12eee1109a
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spelling	oai:doaj.org-article:763a764526024a7ca9ce2b12eee1109a2021-11-08T02:37:06ZEmploying Multimodal Machine Learning for Stress Detection2040-230910.1155/2021/9356452https://doaj.org/article/763a764526024a7ca9ce2b12eee1109a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9356452https://doaj.org/toc/2040-2309In the current information age, the human lifestyle has become more knowledge-oriented, leading to sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected, however crucial, aspects of today’s fast-paced world. Mental health issues can, both directly and indirectly, affect other sections of human physiology and impede an individual’s day-to-day activities and performance. However, identifying the stress and finding the stress trend for an individual that may lead to serious mental ailments is challenging and involves multiple factors. Such identification can be achieved accurately by fusing these multiple modalities (due to various factors) arising from a person’s behavioral patterns. Specific techniques are identified in the literature for this purpose; however, very few machine learning-based methods are proposed for such multimodal fusion tasks. In this work, a multimodal AI-based framework is proposed to monitor a person’s working behavior and stress levels. We propose a methodology for efficiently detecting stress due to workload by concatenating heterogeneous raw sensor data streams (e.g., face expressions, posture, heart rate, and computer interaction). This data can be securely stored and analyzed to understand and discover personalized unique behavioral patterns leading to mental strain and fatigue. The contribution of this work is twofold: firstly, proposing a multimodal AI-based strategy for fusion to detect stress and its level and, secondly, identifying a stress pattern over a period of time. We were able to achieve 96.09% accuracy on the test set in stress detection and classification. Further, we were able to reduce the stress scale prediction model loss to 0.036 using these modalities. This work can prove important for the community at large, specifically those working sedentary jobs, to monitor and identify stress levels, especially in current times of COVID-19.Rahee WalambePranav NayakAshmit BhardwajKetan KotechaHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Rahee Walambe Pranav Nayak Ashmit Bhardwaj Ketan Kotecha Employing Multimodal Machine Learning for Stress Detection
description	In the current information age, the human lifestyle has become more knowledge-oriented, leading to sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected, however crucial, aspects of today’s fast-paced world. Mental health issues can, both directly and indirectly, affect other sections of human physiology and impede an individual’s day-to-day activities and performance. However, identifying the stress and finding the stress trend for an individual that may lead to serious mental ailments is challenging and involves multiple factors. Such identification can be achieved accurately by fusing these multiple modalities (due to various factors) arising from a person’s behavioral patterns. Specific techniques are identified in the literature for this purpose; however, very few machine learning-based methods are proposed for such multimodal fusion tasks. In this work, a multimodal AI-based framework is proposed to monitor a person’s working behavior and stress levels. We propose a methodology for efficiently detecting stress due to workload by concatenating heterogeneous raw sensor data streams (e.g., face expressions, posture, heart rate, and computer interaction). This data can be securely stored and analyzed to understand and discover personalized unique behavioral patterns leading to mental strain and fatigue. The contribution of this work is twofold: firstly, proposing a multimodal AI-based strategy for fusion to detect stress and its level and, secondly, identifying a stress pattern over a period of time. We were able to achieve 96.09% accuracy on the test set in stress detection and classification. Further, we were able to reduce the stress scale prediction model loss to 0.036 using these modalities. This work can prove important for the community at large, specifically those working sedentary jobs, to monitor and identify stress levels, especially in current times of COVID-19.
format	article
author	Rahee Walambe Pranav Nayak Ashmit Bhardwaj Ketan Kotecha
author_facet	Rahee Walambe Pranav Nayak Ashmit Bhardwaj Ketan Kotecha
author_sort	Rahee Walambe
title	Employing Multimodal Machine Learning for Stress Detection
title_short	Employing Multimodal Machine Learning for Stress Detection
title_full	Employing Multimodal Machine Learning for Stress Detection
title_fullStr	Employing Multimodal Machine Learning for Stress Detection
title_full_unstemmed	Employing Multimodal Machine Learning for Stress Detection
title_sort	employing multimodal machine learning for stress detection
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/763a764526024a7ca9ce2b12eee1109a
work_keys_str_mv	AT raheewalambe employingmultimodalmachinelearningforstressdetection AT pranavnayak employingmultimodalmachinelearningforstressdetection AT ashmitbhardwaj employingmultimodalmachinelearningforstressdetection AT ketankotecha employingmultimodalmachinelearningforstressdetection
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Employing Multimodal Machine Learning for Stress Detection

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