On Computer Mouse Pointing Model Online Identification and Endpoint Prediction
| العنوان: | On Computer Mouse Pointing Model Online Identification and Endpoint Prediction |
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| المؤلفون: | Anatolii Khalin, Rosane Ushirobira, Denis Efimov, Gery Casiez |
| المساهمون: | Finite-time control and estimation for distributed systems (VALSE), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Technology and knowledge for interaction (LOKI) |
| المصدر: | IEEE Transactions on Human-Machine Systems IEEE Transactions on Human-Machine Systems, IEEE, 2021 IEEE Transactions on Human-Machine Systems, 2022, 52 (5) |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Human-Computer Interaction, Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering, Signal Processing, Human Factors and Ergonomics, [SPI.AUTO]Engineering Sciences [physics]/Automatic, Computer Science Applications |
| الوصف: | International audience; This paper proposes a new simplified pointing model as a feedback-based dynamical system, including both human and computer sides of the process. It takes into account the commutation between the correction and ballistic phases in pointing tasks. We use the mouse position increment signal from noisy experimental data to achieve our main objectives: to estimate the model parameters online and predict the task endpoint. Some estimation tools and validation results, applying linear regression techniques on the experimental data are presented. We also compare with a similar prediction algorithm to show the potential of our algorithm's implementation. |
| تدمد: | 2168-2305 2168-2291 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68e258037c0985b8e7e64775d8952fd0 https://doi.org/10.1109/thms.2021.3131660 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....68e258037c0985b8e7e64775d8952fd0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21682305 21682291 |
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