Employing Multimodal Machine Learning for Stress Detection
This work addresses stress monitoring for individuals in sedentary jobs, particularly relevant during COVID-19, but it is incremental as it builds on existing multimodal fusion techniques.
The paper tackles stress detection by proposing a multimodal AI framework that fuses heterogeneous sensor data streams, achieving 96.09% accuracy in stress detection and classification and reducing stress scale prediction model loss to 0.036.
In the current age, human lifestyle has become more knowledge oriented leading to generation of sedentary employment. This has given rise to a number of health and mental disorders. Mental wellness is one of the most neglected but crucial aspects of today's 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 leading 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 behavioral patterns. Certain 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, 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; proposing a multimodal AI-based strategy for fusion to detect stress and its level and secondly identify 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 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.