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Learning-Based Adaptation for Personalized Mobility Assistance

Área de investigación: Inteligencia Computacional, Inteligencia Ambiental Año: 2013
Tipo de publicación: Conferencia
Autores: Urdiales García, Cristina; Peula Palacios, José Manuel; Fernández Carmona, Manuel; Sandoval Hernández, Francisco
Editor: (Sarah Jane Delany and Santiago Ontañón, Eds.)
Serie: LNAI 7969, pp. 329-342
Publisher: Springer-Verlag Berlin Heidelberg
ISBN: 9783642390555 ISSN: 0302-9743
Mobility assistance is of key importance for people with dis-
abilities to remain autonomous in their preferred environments. In severe
cases, assistance can be provided by robotized wheelchairs that can per-
form complex maneuvers and/or correct the user’s commands. User’s ac-
ceptance 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.