Accurate Stress detection from Novel Real-Time Electrodermal Activity Signals and Multi-task Learning Models

Abstract

Stress often is associated with physical and mental health issues. To prevent these issues, an early detection of stress is essential. However, for people with an intellectual disability effectively expressing stress can be difficult and therefore, the necessary intervention can be delayed. An automatic stress detection system could help caregivers in early detection of stress development. This can be achieved using wearable sensors that continuously record physiology. The changes in physiological signals, like in skin conductance can be used to classify moments of stress. The devices recording these signals are however, not always suitable for long term measurements. The present study evaluates a newly developed sock integrated skin conductance sensor (SentiSock) that does not restrict movement and stays comfortable over time. To assess if the sensor can be used for stress detection a comparison was made with the Empatica E4, a commonly used wrist-based skin conductance sensor. Both sensors were worn by 28 participants (mean age 39.25 ± 17.04) in a lab study where stress was induced using mathematical exercises. The data was used to train a multitask learning neural network for each device, following an identical procedure. The models were validated using a 5-fold cross validation that resulted in an average balanced accuracy of 0.824 (SD = 0.018) for Empatica E4 and 0.834 (SD = 0.019) for SentiSock. This demonstrated that both sensors can be used to detect stress adequately in lab conditions. Given these results, SentiSock will be further investigated for long term measurements.