@inproceedings{BieberHarmsLeppichetal.2024, author = {Bieber, Carina and Harms, Patrick and Leppich, Dominick and Proschek, Katrin}, title = {AI-based User Emotion Recognition from Interaction Data : Challenges and Guidelines for Training Data Creation}, series = {MODELS Companion ´24}, booktitle = {MODELS Companion ´24}, doi = {10.1145/3652620.3686245}, pages = {242 - 252}, year = {2024}, abstract = {Artificial Intelligence (AI) is a rising topic in the field of emotion recognition, e.g., from facial expressions. However, existing methods often require to be performed in staged set ups and are obtrusive by gathering additional data. Especially, collecting video data includes a high data protection risk. Our approach is to provide an unobtrusive emotion recognition tool based on Keystroke, Mouse and Touchscreen (KMT) data. Recently, we published a data set for emotion recognition from keystroke and mouse interaction data. In this paper, we present the challenges we faced during the creation of the data set. This covers collecting User Interface (UI) data as well as emotional ground truth data. For each of seven mentioned challenges, we provide our solutions as well as guidelines for other researchers to prevent them. The challenges include possible issues with recorded data as well as issues of automated facial coding engines. We provide a possible approach for manual facial coding and describe aspects attention should be paid to. Furthermore, we indicate issues when using different software tools to collect the data. The paper aims to help other researchers by providing insights and a guideline for the creation of the data set. We make these insights available for other researchers who want to create similar data sets or who want to expand ours. By sharing our insights, we aim to improve the reproducibility of AI training data creation and AI-based emotion recognition from user interaction data.}, language = {en} } @article{LeppichBieberProscheketal.2023, author = {Leppich, Dominick and Bieber, Carina and Proschek, Katrin and Harms, Patrick and Schubert, Ulf}, title = {DUX : a dataset of user interactions and user emotions}, series = {i-com : Journal of Interactive Media}, journal = {i-com : Journal of Interactive Media}, number = {2}, publisher = {Oldenburg Wissenschaftsverlag}, address = {Oldenburg}, issn = {2196-6826}, doi = {10.1515/icom-2023-0014}, pages = {23}, year = {2023}, abstract = {User experience evaluation is becoming increasingly important, and so is emotion recognition. Recognizing users' emotions based on their interactions alone would not be intrusive to users and can be implemented in many applications. This is still an area of active research and requires data containing both the user interactions and the corresponding emotions. Currently, there is no public dataset for emotion recognition from keystroke, mouse and touchscreen dynamics. We have created such a dataset for keyboard and mouse interactions through a dedicated user study and made it publicly available for other researchers. This paper examines our study design and the process of creating the dataset. We conducted the study using a test application for travel expense reports with 50 participants. We want to be able to detect predominantly negative emotions, so we added emotional triggers to our test application. However, further research is needed to determine the relationship between user interactions and emotions.}, language = {en} } @inproceedings{LeppichHarms2022, author = {Leppich, Dominick and Harms, Patrick}, title = {Automated Usability Smell Detection in VR Applications with AutoQUEST}, doi = {10.1145/3531706.3536449}, pages = {59 - 60}, year = {2022}, abstract = {The quality of software products is not only measured by their set of functionality and features, the User Experience (UX) and the quality of the applications' User Interface (UI) gets more and more important. In order to measure and improve the usability of UIs, it is often required to perform user tests with potential future users. Performing these user tests, taking notes meanwhile and analyzing all the collected data to derive some meaningful results can be a time-consuming task. An automated approach to evaluate the usability of UIs may save time and help to improve the UX. In this tutorial, we present AutoQUEST, a set of tools to automatically record user interaction data and perform automated usability evaluation. The evaluation is done by detecting common interaction structures and assessing them with respect to patterns representing known UX issues. We will demonstrate this usability evaluation technique on an existing study of a Virtual Reality (VR) application of our own.}, language = {en} } @inproceedings{HarmsLeppich2022, author = {Harms, Patrick and Leppich, Dominick}, title = {Creating Virtual Prototypes of Technical Devices using Vivifly}, doi = {10.1145/3531706.3536451}, pages = {61 - 62}, year = {2022}, abstract = {When developing technical devices, such as home appliances, their user interfaces must be evaluated with respect to usability and user experience. For this, companies create expensive real world prototypes of these devices, ask users to interact with them, and record any issues the users have. Virtual Prototypes (VPs) provided in eXtended Reality (XR) may serve the same purpose with the advantage of being cheaper and more widespread available. Unfortunately, the creation of VPs, especially for multi-platform XR, is nowadays challenging and requires game programming skills. In this tutorial, we present Vivifly and Vivian, two tools for configuring and running simple VPs in different variants of XR. Through this, companies can easily create VPs for their devices under development and test them with users in mobile Augmented Reality (AR), in Virtual Reality (VR), or in Mixed Reality (MR) using a single configuration.}, language = {de} }