Trajectory Clustering for the Classification of Eye-Tracking Users With Motor Disorders
Use este enlace para citar
http://hdl.handle.net/2183/29567
A non ser que se indique outra cousa, a licenza do ítem descríbese como Atribución-NoComercial-CompartirIgual 4.0 Internacional
Coleccións
Metadatos
Mostrar o rexistro completo do ítemTítulo
Trajectory Clustering for the Classification of Eye-Tracking Users With Motor DisordersAutor(es)
Data
2016Cita bibliográfica
Clemotte, A., Arregui, H., Velasco, M.A., Unzueta, L., Goenetxea, J., Elordi, U., Rocon, E., Ceres, R., Bengoechea, J., Arizkuren, I., Jauregui, E. Trajectory clustering for the classification of eye-tracking users with motor disorders. En Actas de las XXXVII Jornadas de Automática. 7, 8 y 9 de septiembre de 2016, Madrid (pp. 150-155). DOI capítulo: https://doi.org/10.17979/spudc.9788497498081.0150 DOI libro: https://doi.org/10.17979/spudc.9788497498081
Resumo
[Abstract] This paper presents a pilot study completed in the framework of the INTERAAC project. The aim of the project is to develop a new human-computer interaction (HCI) solution based on eye-gaze estimation from webcam images for people with motor disorders such as cerebral palsy, neurodegenerative diseases, and spinal cord injury that are otherwise unable to use a keyboard or mouse. In this study, we analyzed cursor trajectories recorded during the experiment and validated that users with different diseases can be automatically classi ed in groups based on trajectory metrics. For the clustering, Ward's method was used. The metrics are based on speed and acceleration statistics from full fi ltered tracks. The results show that the participants can be grouped into two main clusters. The main contribution of this work is the evaluation of the clustering techniques applied to eye-gaze trajecto- ries for the automatic classi cation of users diseases based on a real experiment carried with the help of three clinical partners in Spain.
Palabras chave
Eye-gaze estimation
Motor disorder
User-type classification
Trajectory clustering
Motor disorder
User-type classification
Trajectory clustering
Versión do editor
Dereitos
Atribución-NoComercial-CompartirIgual 4.0 Internacional
ISBN
978-84-617-4298-1 (UCM) 978-84-9749-808-1 (UDC electrónico)