Title
Learning-Based Adaptation for Personalized Mobility Assistance.
Abstract
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.
Year
DOI
Venue
2013
10.1007/978-3-642-39056-2_24
ICCBR
Keywords
Field
DocType
Shared Control,Cluster Prototype,Case Base Reasoning System,Global Path Planning,Power Wheelchair
Computer science,Artificial intelligence,Robot,Case-based reasoning,Multimedia,Robotics
Conference
Citations 
PageRank 
References 
1
0.36
11
Authors
4
Name
Order
Citations
PageRank
C. Urdiales125133.14
Jose Manuel Peula2275.49
Manuel Fernández-Carmona3163.66
Francisco Sandoval Hernández4771104.15