Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Intelligence without representation
Artificial Intelligence
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Explanation-Driven Case-Based Reasoning
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
An Architecture for a CBR Image Segmentation System
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Interaction and intelligent behavior
Interaction and intelligent behavior
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Medical applications in case-based reasoning
The Knowledge Engineering Review
Emergent (mis)behavior vs. complex software systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
International Journal of Intelligent Systems Technologies and Applications
A multi-agent based infrastructure to support virtual communities in elderly care
International Journal of Networking and Virtual Organisations
Case-Based Reasoning in the Health Sciences: Why It Matters for the Health Sciences and for CBR
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Stratified case-based reasoning: reusing hierarchical problem solving episodes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Case-based reasoning in the health sciences: What's next?
Artificial Intelligence in Medicine
Case-based object recognition for airborne fungi recognition
Artificial Intelligence in Medicine
Integrating behavioral, perceptual, and world knowledge in reactive navigation
Robotics and Autonomous Systems
CADI: an intelligent, multimedia tutor for cardiac auscultation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Biologically-inspired visual-motor coordination model in a navigation problem
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Objective: Testing is a key stage in system development, particularly in systems such as a wheelchair, in which the final user is typically a disabled person. These systems have stringent safety requirements, requiring major testing with many different individuals. The best would be to have the wheelchair tested by many different end users, as each disability affects driving skills in a different way. Unfortunately, from a practical point of view it is difficult to engage end users as beta testers. Hence, testing often relies on simulations. Naturally, these simulations need to be as realistic as possible to make the system robust and safe before real tests can be accomplished. This work presents a tool to automatically test wheelchairs through realistic emulation of different wheelchair users. Methods and materials: 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. Results: The proposed system has been used to emulate several power wheelchair users presenting different disabilities. Data to create this emulation was obtained from previous wheelchair navigation experiments with 35 volunteer in-patients presenting different degrees of disability. CBR was trained with a limited number of scenarios for each volunteer. Results proved that: (i) emulated and real users returned similar paths in the same scenario (maximum and mean path deviations are equal to 23 and 10cm, respectively) and similar efficiency; (ii) we established the generality of our approach taking a new path not present in the training traces; (iii) the emulated user is more realistic - path and efficiency are less homogeneous and smooth - than potential field approaches; and (iv) the system adequately emulates in-patients - maximum and mean path deviations are equal to 19 and 8.3cm approximately and efficiencies are similar - with specific disabilities (apraxia and dementia) obtaining different behaviors during emulation for each of the in-patients, as expected. Conclusions: The proposed system adequately emulates the driving behavior of people with different disabilities in indoor scenarios. This approach is suitable to emulate real users' driving behaviors for early testing stages of assistive navigation systems.