When mental workload increases, mental fatigue and stress levels increase, and mental health starts deteriorating. Quantifying mental workload is essential to intervene wide range of mental health-related diseases. Researchers have been working on measuring mental workload for decades but mostly in controlled environments. However, only a few works have been done in daily life settings. In this project, we have collected Electroencephalography (EEG), Photoplethysmography (PPG), and Electrodermal Activity (EDA) sensor data from 20 participants while eliciting mental workload in a controlled environment and also while performing their own chosen tasks in daily life. The potential of this project is to classify mental workload utilizing multimodal sensors in a controlled environment and in daily life scenarios. The latter will be one of the most challenging aspects we focus on. Moreover, we would develop a feature-based approach to investigate the most correlated physiological parameters for mental workload detection.
Current Status (January 2022)
We are processing and analyzing the collected data from 20 participants for classification and plan to extend the data collection. We are looking for Masters's students to work on this data as a part of their master's thesis.
Towards Multi-Modal Recordings in Daily Life: A Baseline Assessment of an Experimental Framework; Christoph Anders, Sidratul Moontaha, Bert Arnrich; Pervasive Health and Smart Sensing at the Information Society; 2022-10; https://is.ijs.si/wp-content/uploads/2022/is2022zborniki/IS2022_Volume-H.pdf