Abstract
A large dataset of publicly available input data was released recently, containing mouse movements from hundreds of crowdsourced participants including those with and without self-reported motor impairments. This paper provides further analysis of that dataset, in particular by segmenting the input device between physical desktop mice and laptop touchpads. The input device as an independent variable provides significant differences in several input tasks, indicating that the input modality variable is as important as the previously reported analyzed variables. Our analysis provides additional validity to the dataset and its existing analysis based on self-reported motor impairment and participant age.
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NSF support for this project is acknowledged and greatly appreciated (#IIS-1551590).
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Pham, L., Whitlow, S., Rosenbaum, E., Magee, J. (2022). Input Accessibility: Effect of Input Device on Interaction Time and Accuracy - An Expanded Analysis of a Large Dataset. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_77
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