Print Email Facebook Twitter A novel, simple and objective method to detect movement artefacts in electrodermal activity Title A novel, simple and objective method to detect movement artefacts in electrodermal activity Author Thammasan, N. Stuldreher, I. Wismeijer, D. Poel, M. van Erp, J. Brouwer, A.M. Publication year 2019 Abstract Skin conductance or electrodermal activity (EDA) has been shown to be a reliable indicator of emotional arousal. With the current development to record EDA with wearable sensors attached to individuals who freely move around, detecting EDA artefacts is essential. Current practice of detecting EDA artefacts relies heavily on expert judgments. This is undesirable, especially when facing large amounts of EDA data for which the exact recording context is unknown or for real-time applications. We propose to exploit the similarity in EDA as measured from two hands to determine artefacts in a simple and objective way. The basic idea is that fast EDA fluctuations that are similar in both hands are caused by emotional processes, while dissimilar fluctuations indicate that the sensor at one of the hands suffers from an artefact. This idea was tested in an experiment where participants were asked to make certain movements with their right hand at designated times while not moving the left hand. Besides eliciting movement artefacts in this way, emotional sounds were presented to elicit arousal responses. When both hands were stationary, right-left hand difference signals were flatter compared to raw signals of the left hand, confirming the logic of our approach. We tested first versions of algorithms based on signal synchrony to automatically detect movement artefacts. To evaluate the performance of these algorithms, we did not rely on expert judgment but on the ground truth of no movement artefacts in the stationary epochs. We conclude that two-hand EDA can be valuable for more objective and less labor intensive EDA artefact detection, which is important for EDA studies out of the laboratory. Subject Healthy for LifeHealthy LivingCorrelationWristAccelerationElectrodesFeature extractionCognitive systemsPresseslectrodermal activityMovement artefactEmotionCorrelationWearable sensor To reference this document use: http://resolver.tudelft.nl/uuid:247a312e-f246-40f5-9279-84fabcb8c783 DOI https://doi.org/10.1109/acii.2019.8925512 TNO identifier 873364 ISBN 9781728138886 Source 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), 371-377 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.