Electrodermal activity (EDA) is a physiological signal that can be used to infer humans' affective states and stress levels. EDA can nowadays be monitored using unobtrusive wearable devices, such as smartwatches, and leveraged in personal informatics systems. A still largely uncharted issue concerning EDA is the impact on real applications of potential differences observable on signals measured concurrently on the left and right side of the human body. This phenomenon, called lateralization, originates from the distinct functions that the brain's left and right hemispheres exert on EDA. In this work, we address this issue by examining the impact of EDA lateralization in two classification tasks: a cognitive load recognition task executed in the lab and a sleep monitoring task in a real-world setting. We implement a machine learning pipeline to compare the performance obtained on both classification tasks using EDA data collected from the left and right sides of the body. Our results show that using EDA from the side that is not associated with the specific hemisphere activation leads to a significant decline in performance for the considered classification tasks. This finding highlights that researchers and practitioners relying on EDA data should consider possible EDA lateralization effects when deciding on sensor placement.

Sleep quality has a significant impact on human’s physical and mental well-being. While there exist several definitions of what makes a good quantity and quality of sleep, such measures are not always in agreement with each other. Further, they often do not match with the human perception of a restorative sleep. In this paper, we investigate the correlation between objective sleep quality measurements – which can be sensed using wearable devices – and the sleeper’s subjective – i.e., self-reported – sleep quality. We analyze the M2Sleep dataset, which contains sleep data of 16 participants over one month, and show that only few sensor-based measures (sleep duration, skin temperature, tonic component of electrodermal activity, acceleration skewness and kurtosis) and personality-based features (extroversion, agreeableness, PSQI score) correlate with self-reported sleep quality. These results may inform the design of future studies on sleep quality assessment.

Engagement is a human experience relevant in multiple contexts, including classrooms, presentations and workplaces. Stemming from flow theory, engagement in these contexts has been studied using wearable devices, which can unobtrusively measure physiological changes, specifically Electrodermal Activity (EDA). However, researchers have not explored how EDA markers might be similar or different between various engagement scenarios, namely student, audience and workplace engagement. In this study, we investigated possible similarities through the use of three datasets containing EDA data and engagement self-report labels, collected in the wild in different settings using research-grade wrist-worn wearable devices. We analysed the correlation between hand-crafted EDA features and the engagement level and we leveraged a machine learning framework for engagement prediction. We found that similar features are correlated with the engagement level across the various settings. We also found that our machine learning model identified related markers as important across the three engagement contexts. Our results highlight that similarities are present in the EDA features between different engagement contexts, while also identifying possible dataset specific differences.