A usability study of physiological measurement in school using wearable sensors

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students' physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps

A comparison of different electrodermal variables in response to an acute social stressor

We investigated electrodermal activity (EDA) in 130 participants undergoing a shortened version of a novel easy, effective and controlled method to induce stress (the Sing-a-Song Stress Test). We compared skin conductance level (SCL), amplitude and number of skin conductance response peaks with respect to their sensitivity to the known stressor, for different scenarios of interests. EDA increased after stressor-onset for almost all participants. At a group level, the three variables were about equally sensitive. When examining the increase following the stressor with respect to preceding EDA within one individual, peak amplitude was most sensitive. Peak measures were clearly most sensitive in a simulated between-subject scenario (i.e., testing the difference in EDA between stress and non-stress intervals as if data originated from different, stressed and non-stressed groups of individuals). Peaks can be extracted by continuous decomposition (CDA) or through-to-peak analysis (TTP). In all analyses performed, CDA outperformed TTP. We thus recommend CDA peak amplitude for monitoring physiological stress effects in e.g. symbiotic systems