2026-05-30T10:49:03Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/342692026-02-03T11:04:58Zcom_10630_2254col_10630_37953

Peula Palacios, José Manuel Urdiales-García, Amalia Cristina Herrero-Reder, Ignacio Fernández-Carmona, Manuel Sandoval-Hernández, Francisco 2024-10-03T10:12:57Z 2024-10-03T10:12:57Z 2012 https://hdl.handle.net/10630/34269 10.1016/j.artmed.2012.08.007 Our approach is based on extracting meaningful data from real users driving a power wheelchair autonomously. This data is then used to train a case-based reasoning (CBR) system that captures the specifics of the driver via learning. The resulting case-base is then used to emulate the driving behavior of that specific person in more complex situations or when a new assistive algorithm needs to be tested. CBR returns user's motion commands appropriate for each specific situation to add the human component to shared control systems. eng open access Sillas de ruedas Inteligencia artificial - Aplicaciones médicas Case-based reasoning emulation of persons for wheelchair navigation. journal article