Prof. Dr.-Ing. Bert Arnrich

Development and Validation of a Multimodal Data Acquisition and Analysis Platform for Assessing Workplace Stress in Group Settings

Sai Siddhant Gadamsetti, Supervisor: Christoph Anders

Master's Thesis

This thesis delves into the evolving dynamics of workplace stress, heightened by rapid technological changes and the global COVID-19 pandemic, emphasizing the necessity for effective stress management strategies. Workplace stress, defined by the World Health Organization as a response to unmanageable work demands, significantly affects both individual well-being and organizational productivity. The psychological and physiological impacts of stress, such as anxiety, depression, and physical health complications, accentuate the urgency for efficacious interventions. A major challenge in this field is the measurement of mental workload and stress. Traditional methods predominantly relied on subjective self-reports, but there has been a shift towards more objective measures, including wearable sensors and biometric monitoring. These newer methods provide a more detailed representation of stress levels but encounter obstacles in their practical application in workplace environments.

Addressing these challenges, this thesis focuses on yoga as a stress management intervention. Yoga, which integrates physical postures, controlled breathing, and meditation, has demonstrated substantial efficacy in reducing workplace stress. Its adaptability renders it suitable across various workplace settings, making it a practical and inclusive option.

In this thesis, the primary focus was to establish the foundational elements for understanding workplace stress and its management. In the scope of my work, I developed a Cognitive Load Induction Task (TLoadDback) to simulate real-world cognitive demands, designed a multimodal data acquisition system to record physiological responses and established a sophisticated data analysis platform. A full-scale pilot study was conducted to validate the developed Cognitive Load Induction Task, data acquisition system, and data analysis platform. The findings from this research could potentially contribute to the field of occupational health, laying the groundwork for future investigations into the management of cognitive workload and the enhancement of employee well-being particularly through interventions like yoga.