Abstract
AbstractThis article introduces a dataset of human-machine interactions collected in a controlled and structured manner. The aim of this dataset is to provide insights into user behavior and support the development of adaptive Human-Machine Interfaces (HMIs). The dataset was generated using a custom-built application that leverages formally defined User Interfaces (UIs). The resulting interactions underwent processing and analysis to create a suitable dataset for professionals and data analysts interested in user interface adaptations. The data processing stage involved cleaning the data, ensuring its consistency and completeness. A data profiling analysis was conducted for checking the consistency of elements in the interaction sequences. Furthermore, for the benefit of researchers, the code used for data collection, data profiling, and usage notes on creating adaptive user interfaces are made available. These resources offer valuable support to those interested in exploring and utilizing the dataset for their research and development efforts in the field of human-machine interfaces.
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference21 articles.
1. Gong, C. Human-machine interface: Design principles of visual information in human-machine interface design. In 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, vol. 2, 262–265 (IEEE, 2009).
2. Aranburu, E., Lasa, G. & Kepa Gerrikagoitia, J. Evaluating the human machine interface experience in industrial workplaces. Proceedings of the 32nd International BCS Human Computer Interaction Conference 32, 1–5 (2018).
3. Champiri, Z. D., Mujtaba, G., Salim, S. S. & Chong, C. Y. User experience and recommender systems. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 1–5 (IEEE, 2019).
4. McComb, C., Cagan, J. & Kotovsky, K. Capturing human sequence-learning abilities in configuration design tasks through markov chains. Journal of Mechanical Design 139 (2017).
5. Carrera-Rivera, A., Larrinaga, F., Lasa, G. & Martinez-Arellano, G. Ux- for smart-pss: Towards a context-aware framework. In Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications - Volume 1: CHIRA, 113–120, https://doi.org/10.5220/0011379700003323 INSTICC (SciTePress, 2022).
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