DigiStress – Understanding the Interdependency between Stress and Digital Technologies
Co-located to PervasiveHealth 2019
In this workshop, we aim to expand and shape the understanding of the interdependency between stress and digital technologies. Facing the ongoing advancements in ubiquitous technologies, this workshop aims to foster reflected discussions but also develop visions on future trends and how to adequately and responsibly deal with stress in the context of digital technologies.
Within this scope, we tackle the two main topics: stress detection using technologies and stressor identification in technology usage. We would like to invite researchers working in the domains of human-computer interaction, computer science, cognitive sciences, psychology, and related disciplines to submit a position paper and/or a demo presentation dealing with topics Detection Algorithms for Sensing Stress, Consumer Devices to Detect Stress, Linking Stress to Activities with Digital Devices, and Stress Generated by Digital Technologies. This could also imply, for example biosensing/feedback, theoretical papers, and specific evaluation methods for stress recognition (applications).
We accept submissions about empirical studies, application prototypes and concepts including but not limited to the following themes:
o Stress detection. How can we accurately detect and measure stress? Advancements in the fields of signal processing, machine learning and intelligent systems have made it possible to detect, quantify and classify stress levels in a continuous fashion. How can we incorporated contextual information stemming from the use of mobile devices to improve stress detection? How can we incorporate known physiological patterns such as the circadian rhythm? o Consumer devices to sense stress. How off-the-shelf wearable devices can be used for detecting stress? Wearable devices (e.g., Empatica) have been used to monitor a wide range of physiological responses, influenced by one’s physical activity, physiological state and induced stress. Which physiological monitoring devices, sensors and setups are reliable enough for inferring stress levels? o Linking Stress to Activities with Digital Devices. Can we associate stress to the use of specific applications? For example, we know that often certain interactions with mobiIe devices are a result of a habit (i.e., habitual interactions), or even direct consequence of one being stressed. However, is it possible that certain (mobile) interactions inflict stress instead (technostress)? What is the impact on user behavior? o Extracting a digital “technostress fingerprint”. Can we infer a “stress index” for certain technological interactions? How can we incorporate such an index in the
design of future systems for eliciting interactions that improve User Experience? How do we communicate stress levels and a potential technostress index to the user?
Workshop papers will be published together with Pervasive Health Proceedings. o All accepted and presented papers will be published by ACM (TBC) and made available through ACM Digital Library. o Proceedings are indexed in leading indexing services, including Ei Compendex, ISI Web of Science, Scopus, CrossRef, Google Scholar, DBLP, as well as EAI’s own EU Digital Library (EUDL). o All accepted authors are eligible to submit an extended version in a fast track of: EAI Endorsed Transactions on Pervasive Health and Technology (http://eudl.eu/issue/phat/4/13)
Please prepare your 4-6 pages paper using the ACM 2-column format. View the ACM guidelines and templates here. (https://www.acm.org/publications/proceedings-template). Please follow the instructions available at http://pervasivehealth.org/authors-kit/.
Submit your paper HERE.
Submission Deadline: 25th February 2019
Notification Deadline: 25th March 2019
Camera-ready Deadline: 31th March 2019
All deadlines refer to the UTC Time Zone.
Romina Poguntke, University of Stuttgart
Katrin Hänsel, Queen Mary University London
Evangelos Niforatos, North Inc., Kitchener ON, Canada
Albrecht Schmidt, Ludwig-Maximilians-Universität München, Munich