Prediction of user preference over shared-control paradigms for a robotic wheelchair

Ahmetcan Erdogan, Brenna Dee Argall

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The design of intelligent powered wheelchairs has traditionally focused heavily on providing effective and efficient navigation assistance. Significantly less attention has been given to the end-user's preference between different assistance paradigms. It is possible to include these subjective evaluations in the design process, for example by soliciting feedback in post-experiment questionnaires. However, constantly querying the user for feedback during real-world operation is not practical. In this paper, we present a model that correlates objective performance metrics and subjective evaluations of autonomous wheelchair control paradigms. Using off-the-shelf machine learning techniques, we show that it is possible to build a model that can predict the most preferred shared-control method from task execution metrics such as effort, safety, performance and utilization. We further characterize the relative contributions of each of these metrics to the individual choice of most preferred assistance paradigm. Our evaluation includes Spinal Cord Injured (SCI) and uninjured subject groups. The results show that our proposed correlation model enables the continuous tracking of user preference and offers the possibility of autonomy that is customized to each user.

Original languageEnglish (US)
Title of host publication2017 International Conference on Rehabilitation Robotics, ICORR 2017
PublisherIEEE Computer Society
Pages1106-1111
Number of pages6
ISBN (Electronic)9781538622964
DOIs
StatePublished - Aug 11 2017
Event2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom
Duration: Jul 17 2017Jul 20 2017

Other

Other2017 International Conference on Rehabilitation Robotics, ICORR 2017
CountryUnited Kingdom
CityLondon
Period7/17/177/20/17

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Rehabilitation

Fingerprint Dive into the research topics of 'Prediction of user preference over shared-control paradigms for a robotic wheelchair'. Together they form a unique fingerprint.

  • Cite this

    Erdogan, A., & Argall, B. D. (2017). Prediction of user preference over shared-control paradigms for a robotic wheelchair. In 2017 International Conference on Rehabilitation Robotics, ICORR 2017 (pp. 1106-1111). [8009397] IEEE Computer Society. https://doi.org/10.1109/ICORR.2017.8009397