RT Journal Article
T1 Learning-Based Adaptation for Personalized Mobility Assistance
A1 Urdiales-García, Amalia Cristina
A1 Peula-Palacios, José Manuel
A1 Fernández-Carmona, Manuel
A1 Sandoval-Hernández, Francisco
K1 Robótica
AB Mobility assistance is of key importance for people with disabilities to remain autonomous in their preferred environments. In severe cases, assistance can be provided by robotized wheelchairs that can perform complex maneuvers and/or correct the user’s commands. User’s acceptance is of key importance, as some users do not like their commands to be modified. This work presents a solution to improve acceptance. It consists of making the robot learn how the user drives so corrections will not be so noticeable to the user. Case Based Reasoning (CBR) is used to acquire a user’s driving model reactive level. Experiments with volunteers at Fondazione Santa Lucia (FSL) have proven that, indeed, this customized approach at assistance increases acceptance by the user.
PB Springer-Verlag
YR 2013
FD 2013
LK http://hdl.handle.net/10630/5712
UL http://hdl.handle.net/10630/5712
LA eng
NO This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia (MEC), Project TEC2011-29106-C02-01. The authors would like to thank Santa Lucia Hospedale and all volunteers for their kind cooperation and Sauer Medica for providing the power wheelchair.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026