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

Ahmetcan Erdogan; Brenna Dee Argall

doi:10.1109/ICORR.2017.8009397

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

Ahmetcan Erdogan, Brenna Dee Argall

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 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 language	English (US)
Title of host publication	2017 International Conference on Rehabilitation Robotics, ICORR 2017
Editors	Arash Ajoudani, Panagiotis Artemiadis, Philipp Beckerle, Giorgio Grioli, Olivier Lambercy, Katja Mombaur, Domen Novak, Georg Rauter, Carlos Rodriguez Guerrero, Gionata Salvietti, Farshid Amirabdollahian, Sivakumar Balasubramanian, Claudio Castellini, Giovanni Di Pino, Zhao Guo, Charmayne Hughes, Fumiya Iida, Tommaso Lenzi, Emanuele Ruffaldi, Fabrizio Sergi, Gim Song Soh, Marco Caimmi, Leonardo Cappello, Raffaella Carloni, Tom Carlson, Maura Casadio, Martina Coscia, Dalia De Santis, Arturo Forner-Cordero, Matthew Howard, Davide Piovesan, Adriano Siqueira, Frank Sup, Masia Lorenzo, Manuel Giuseppe Catalano, Hyunglae Lee, Carlo Menon, Stanisa Raspopovic, Mo Rastgaar, Renaud Ronsse, Edwin van Asseldonk, Bram Vanderborght, Madhusudhan Venkadesan, Matteo Bianchi, David Braun, Sasha Blue Godfrey, Fulvio Mastrogiovanni, Andrew McDaid, Stefano Rossi, Jacopo Zenzeri, Domenico Formica, Nikolaos Karavas, Laura Marchal-Crespo, Kyle B. Reed, Nevio Luigi Tagliamonte, Etienne Burdet, Angelo Basteris, Domenico Campolo, Ashish Deshpande, Venketesh Dubey, Asif Hussain, Vittorio Sanguineti, Ramazan Unal, Glauco Augusto de Paula Caurin, Yasuharu Koike, Stefano Mazzoleni, Hyung-Soon Park, C. David Remy, Ludovic Saint-Bauzel, Nikos Tsagarakis, Jan Veneman, Wenlong Zhang
Publisher	IEEE Computer Society
Pages	1106-1111
Number of pages	6
ISBN (Electronic)	9781538622964
DOIs	https://doi.org/10.1109/ICORR.2017.8009397
State	Published - Aug 11 2017
Event	2017 International Conference on Rehabilitation Robotics, ICORR 2017 - London, United Kingdom Duration: Jul 17 2017 → Jul 20 2017

Publication series

Name	IEEE International Conference on Rehabilitation Robotics
ISSN (Print)	1945-7898
ISSN (Electronic)	1945-7901

Other

Other	2017 International Conference on Rehabilitation Robotics, ICORR 2017
Country/Territory	United Kingdom
City	London
Period	7/17/17 → 7/20/17

Funding

ACKNOWLEDGEMENT This work was supported by a grant from U.S. Office of Naval Research under Award Number N00014-16-1-2247, which we gratefully acknowledge. The authors would like to thank Jessica P. Pedersen OTR/L, ATP/SMS for her assistance with patient recruitment and experimental supervision.

ASJC Scopus subject areas

Control and Systems Engineering
Rehabilitation
Electrical and Electronic Engineering

Access to Document

10.1109/ICORR.2017.8009397

Cite this

Erdogan, A., & Argall, B. D. (2017). Prediction of user preference over shared-control paradigms for a robotic wheelchair. In A. Ajoudani, P. Artemiadis, P. Beckerle, G. Grioli, O. Lambercy, K. Mombaur, D. Novak, G. Rauter, C. Rodriguez Guerrero, G. Salvietti, F. Amirabdollahian, S. Balasubramanian, C. Castellini, G. Di Pino, Z. Guo, C. Hughes, F. Iida, T. Lenzi, E. Ruffaldi, F. Sergi, G. S. Soh, M. Caimmi, L. Cappello, R. Carloni, T. Carlson, M. Casadio, M. Coscia, D. De Santis, A. Forner-Cordero, M. Howard, D. Piovesan, A. Siqueira, F. Sup, M. Lorenzo, M. G. Catalano, H. Lee, C. Menon, S. Raspopovic, M. Rastgaar, R. Ronsse, E. van Asseldonk, B. Vanderborght, M. Venkadesan, M. Bianchi, D. Braun, S. B. Godfrey, F. Mastrogiovanni, A. McDaid, S. Rossi, J. Zenzeri, D. Formica, N. Karavas, L. Marchal-Crespo, K. B. Reed, N. L. Tagliamonte, E. Burdet, A. Basteris, D. Campolo, A. Deshpande, V. Dubey, A. Hussain, V. Sanguineti, R. Unal, G. A. D. P. Caurin, Y. Koike, S. Mazzoleni, H.-S. Park, C. D. Remy, L. Saint-Bauzel, N. Tsagarakis, J. Veneman, ... W. Zhang (Eds.), 2017 International Conference on Rehabilitation Robotics, ICORR 2017 (pp. 1106-1111). Article 8009397 (IEEE International Conference on Rehabilitation Robotics). IEEE Computer Society. https://doi.org/10.1109/ICORR.2017.8009397

Erdogan, Ahmetcan ; Argall, Brenna Dee. / Prediction of user preference over shared-control paradigms for a robotic wheelchair. 2017 International Conference on Rehabilitation Robotics, ICORR 2017. editor / Arash Ajoudani ; Panagiotis Artemiadis ; Philipp Beckerle ; Giorgio Grioli ; Olivier Lambercy ; Katja Mombaur ; Domen Novak ; Georg Rauter ; Carlos Rodriguez Guerrero ; Gionata Salvietti ; Farshid Amirabdollahian ; Sivakumar Balasubramanian ; Claudio Castellini ; Giovanni Di Pino ; Zhao Guo ; Charmayne Hughes ; Fumiya Iida ; Tommaso Lenzi ; Emanuele Ruffaldi ; Fabrizio Sergi ; Gim Song Soh ; Marco Caimmi ; Leonardo Cappello ; Raffaella Carloni ; Tom Carlson ; Maura Casadio ; Martina Coscia ; Dalia De Santis ; Arturo Forner-Cordero ; Matthew Howard ; Davide Piovesan ; Adriano Siqueira ; Frank Sup ; Masia Lorenzo ; Manuel Giuseppe Catalano ; Hyunglae Lee ; Carlo Menon ; Stanisa Raspopovic ; Mo Rastgaar ; Renaud Ronsse ; Edwin van Asseldonk ; Bram Vanderborght ; Madhusudhan Venkadesan ; Matteo Bianchi ; David Braun ; Sasha Blue Godfrey ; Fulvio Mastrogiovanni ; Andrew McDaid ; Stefano Rossi ; Jacopo Zenzeri ; Domenico Formica ; Nikolaos Karavas ; Laura Marchal-Crespo ; Kyle B. Reed ; Nevio Luigi Tagliamonte ; Etienne Burdet ; Angelo Basteris ; Domenico Campolo ; Ashish Deshpande ; Venketesh Dubey ; Asif Hussain ; Vittorio Sanguineti ; Ramazan Unal ; Glauco Augusto de Paula Caurin ; Yasuharu Koike ; Stefano Mazzoleni ; Hyung-Soon Park ; C. David Remy ; Ludovic Saint-Bauzel ; Nikos Tsagarakis ; Jan Veneman ; Wenlong Zhang. IEEE Computer Society, 2017. pp. 1106-1111 (IEEE International Conference on Rehabilitation Robotics).

@inproceedings{2a7ef1831ee74f9383ef233fc5427053,

title = "Prediction of user preference over shared-control paradigms for a robotic wheelchair",

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.",

author = "Ahmetcan Erdogan and Argall, \{Brenna Dee\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Rehabilitation Robotics, ICORR 2017 ; Conference date: 17-07-2017 Through 20-07-2017",

year = "2017",

month = aug,

day = "11",

doi = "10.1109/ICORR.2017.8009397",

language = "English (US)",

series = "IEEE International Conference on Rehabilitation Robotics",

publisher = "IEEE Computer Society",

pages = "1106--1111",

editor = "Arash Ajoudani and Panagiotis Artemiadis and Philipp Beckerle and Giorgio Grioli and Olivier Lambercy and Katja Mombaur and Domen Novak and Georg Rauter and \{Rodriguez Guerrero\}, Carlos and Gionata Salvietti and Farshid Amirabdollahian and Sivakumar Balasubramanian and Claudio Castellini and \{Di Pino\}, Giovanni and Zhao Guo and Charmayne Hughes and Fumiya Iida and Tommaso Lenzi and Emanuele Ruffaldi and Fabrizio Sergi and Soh, \{Gim Song\} and Marco Caimmi and Leonardo Cappello and Raffaella Carloni and Tom Carlson and Maura Casadio and Martina Coscia and \{De Santis\}, Dalia and Arturo Forner-Cordero and Matthew Howard and Davide Piovesan and Adriano Siqueira and Frank Sup and Masia Lorenzo and Catalano, \{Manuel Giuseppe\} and Hyunglae Lee and Carlo Menon and Stanisa Raspopovic and Mo Rastgaar and Renaud Ronsse and \{van Asseldonk\}, Edwin and Bram Vanderborght and Madhusudhan Venkadesan and Matteo Bianchi and David Braun and Godfrey, \{Sasha Blue\} and Fulvio Mastrogiovanni and Andrew McDaid and Stefano Rossi and Jacopo Zenzeri and Domenico Formica and Nikolaos Karavas and Laura Marchal-Crespo and Reed, \{Kyle B.\} and Tagliamonte, \{Nevio Luigi\} and Etienne Burdet and Angelo Basteris and Domenico Campolo and Ashish Deshpande and Venketesh Dubey and Asif Hussain and Vittorio Sanguineti and Ramazan Unal and Caurin, \{Glauco Augusto de Paula\} and Yasuharu Koike and Stefano Mazzoleni and Hyung-Soon Park and Remy, \{C. David\} and Ludovic Saint-Bauzel and Nikos Tsagarakis and Jan Veneman and Wenlong Zhang",

booktitle = "2017 International Conference on Rehabilitation Robotics, ICORR 2017",

address = "United States",

}

Erdogan, A & Argall, BD 2017, Prediction of user preference over shared-control paradigms for a robotic wheelchair. in A Ajoudani, P Artemiadis, P Beckerle, G Grioli, O Lambercy, K Mombaur, D Novak, G Rauter, C Rodriguez Guerrero, G Salvietti, F Amirabdollahian, S Balasubramanian, C Castellini, G Di Pino, Z Guo, C Hughes, F Iida, T Lenzi, E Ruffaldi, F Sergi, GS Soh, M Caimmi, L Cappello, R Carloni, T Carlson, M Casadio, M Coscia, D De Santis, A Forner-Cordero, M Howard, D Piovesan, A Siqueira, F Sup, M Lorenzo, MG Catalano, H Lee, C Menon, S Raspopovic, M Rastgaar, R Ronsse, E van Asseldonk, B Vanderborght, M Venkadesan, M Bianchi, D Braun, SB Godfrey, F Mastrogiovanni, A McDaid, S Rossi, J Zenzeri, D Formica, N Karavas, L Marchal-Crespo, KB Reed, NL Tagliamonte, E Burdet, A Basteris, D Campolo, A Deshpande, V Dubey, A Hussain, V Sanguineti, R Unal, GADP Caurin, Y Koike, S Mazzoleni, H-S Park, CD Remy, L Saint-Bauzel, N Tsagarakis, J Veneman & W Zhang (eds), 2017 International Conference on Rehabilitation Robotics, ICORR 2017., 8009397, IEEE International Conference on Rehabilitation Robotics, IEEE Computer Society, pp. 1106-1111, 2017 International Conference on Rehabilitation Robotics, ICORR 2017, London, United Kingdom, 7/17/17. https://doi.org/10.1109/ICORR.2017.8009397

Prediction of user preference over shared-control paradigms for a robotic wheelchair. / Erdogan, Ahmetcan; Argall, Brenna Dee.
2017 International Conference on Rehabilitation Robotics, ICORR 2017. ed. / Arash Ajoudani; Panagiotis Artemiadis; Philipp Beckerle; Giorgio Grioli; Olivier Lambercy; Katja Mombaur; Domen Novak; Georg Rauter; Carlos Rodriguez Guerrero; Gionata Salvietti; Farshid Amirabdollahian; Sivakumar Balasubramanian; Claudio Castellini; Giovanni Di Pino; Zhao Guo; Charmayne Hughes; Fumiya Iida; Tommaso Lenzi; Emanuele Ruffaldi; Fabrizio Sergi; Gim Song Soh; Marco Caimmi; Leonardo Cappello; Raffaella Carloni; Tom Carlson; Maura Casadio; Martina Coscia; Dalia De Santis; Arturo Forner-Cordero; Matthew Howard; Davide Piovesan; Adriano Siqueira; Frank Sup; Masia Lorenzo; Manuel Giuseppe Catalano; Hyunglae Lee; Carlo Menon; Stanisa Raspopovic; Mo Rastgaar; Renaud Ronsse; Edwin van Asseldonk; Bram Vanderborght; Madhusudhan Venkadesan; Matteo Bianchi; David Braun; Sasha Blue Godfrey; Fulvio Mastrogiovanni; Andrew McDaid; Stefano Rossi; Jacopo Zenzeri; Domenico Formica; Nikolaos Karavas; Laura Marchal-Crespo; Kyle B. Reed; Nevio Luigi Tagliamonte; Etienne Burdet; Angelo Basteris; Domenico Campolo; Ashish Deshpande; Venketesh Dubey; Asif Hussain; Vittorio Sanguineti; Ramazan Unal; Glauco Augusto de Paula Caurin; Yasuharu Koike; Stefano Mazzoleni; Hyung-Soon Park; C. David Remy; Ludovic Saint-Bauzel; Nikos Tsagarakis; Jan Veneman; Wenlong Zhang. IEEE Computer Society, 2017. p. 1106-1111 8009397 (IEEE International Conference on Rehabilitation Robotics).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Prediction of user preference over shared-control paradigms for a robotic wheelchair

AU - Erdogan, Ahmetcan

AU - Argall, Brenna Dee

PY - 2017/8/11

Y1 - 2017/8/11

N2 - 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.

AB - 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.

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DO - 10.1109/ICORR.2017.8009397

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AN - SCOPUS:85034823761

T3 - IEEE International Conference on Rehabilitation Robotics

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A2 - Ajoudani, Arash

A2 - Artemiadis, Panagiotis

A2 - Beckerle, Philipp

A2 - Grioli, Giorgio

A2 - Lambercy, Olivier

A2 - Mombaur, Katja

A2 - Novak, Domen

A2 - Rauter, Georg

A2 - Rodriguez Guerrero, Carlos

A2 - Salvietti, Gionata

A2 - Amirabdollahian, Farshid

A2 - Balasubramanian, Sivakumar

A2 - Castellini, Claudio

A2 - Di Pino, Giovanni

A2 - Guo, Zhao

A2 - Hughes, Charmayne

A2 - Iida, Fumiya

A2 - Lenzi, Tommaso

A2 - Ruffaldi, Emanuele

A2 - Sergi, Fabrizio

A2 - Soh, Gim Song

A2 - Caimmi, Marco

A2 - Cappello, Leonardo

A2 - Carloni, Raffaella

A2 - Carlson, Tom

A2 - Casadio, Maura

A2 - Coscia, Martina

A2 - De Santis, Dalia

A2 - Forner-Cordero, Arturo

A2 - Howard, Matthew

A2 - Piovesan, Davide

A2 - Siqueira, Adriano

A2 - Sup, Frank

A2 - Lorenzo, Masia

A2 - Catalano, Manuel Giuseppe

A2 - Lee, Hyunglae

A2 - Menon, Carlo

A2 - Raspopovic, Stanisa

A2 - Rastgaar, Mo

A2 - Ronsse, Renaud

A2 - van Asseldonk, Edwin

A2 - Vanderborght, Bram

A2 - Venkadesan, Madhusudhan

A2 - Bianchi, Matteo

A2 - Braun, David

A2 - Godfrey, Sasha Blue

A2 - Mastrogiovanni, Fulvio

A2 - McDaid, Andrew

A2 - Rossi, Stefano

A2 - Zenzeri, Jacopo

A2 - Formica, Domenico

A2 - Karavas, Nikolaos

A2 - Marchal-Crespo, Laura

A2 - Reed, Kyle B.

A2 - Tagliamonte, Nevio Luigi

A2 - Burdet, Etienne

A2 - Basteris, Angelo

A2 - Campolo, Domenico

A2 - Deshpande, Ashish

A2 - Dubey, Venketesh

A2 - Hussain, Asif

A2 - Sanguineti, Vittorio

A2 - Unal, Ramazan

A2 - Caurin, Glauco Augusto de Paula

A2 - Koike, Yasuharu

A2 - Mazzoleni, Stefano

A2 - Park, Hyung-Soon

A2 - Remy, C. David

A2 - Saint-Bauzel, Ludovic

A2 - Tsagarakis, Nikos

A2 - Veneman, Jan

A2 - Zhang, Wenlong

PB - IEEE Computer Society

T2 - 2017 International Conference on Rehabilitation Robotics, ICORR 2017

Y2 - 17 July 2017 through 20 July 2017

ER -

Erdogan A, Argall BD. Prediction of user preference over shared-control paradigms for a robotic wheelchair. In Ajoudani A, Artemiadis P, Beckerle P, Grioli G, Lambercy O, Mombaur K, Novak D, Rauter G, Rodriguez Guerrero C, Salvietti G, Amirabdollahian F, Balasubramanian S, Castellini C, Di Pino G, Guo Z, Hughes C, Iida F, Lenzi T, Ruffaldi E, Sergi F, Soh GS, Caimmi M, Cappello L, Carloni R, Carlson T, Casadio M, Coscia M, De Santis D, Forner-Cordero A, Howard M, Piovesan D, Siqueira A, Sup F, Lorenzo M, Catalano MG, Lee H, Menon C, Raspopovic S, Rastgaar M, Ronsse R, van Asseldonk E, Vanderborght B, Venkadesan M, Bianchi M, Braun D, Godfrey SB, Mastrogiovanni F, McDaid A, Rossi S, Zenzeri J, Formica D, Karavas N, Marchal-Crespo L, Reed KB, Tagliamonte NL, Burdet E, Basteris A, Campolo D, Deshpande A, Dubey V, Hussain A, Sanguineti V, Unal R, Caurin GADP, Koike Y, Mazzoleni S, Park HS, Remy CD, Saint-Bauzel L, Tsagarakis N, Veneman J, Zhang W, editors, 2017 International Conference on Rehabilitation Robotics, ICORR 2017. IEEE Computer Society. 2017. p. 1106-1111. 8009397. (IEEE International Conference on Rehabilitation Robotics). doi: 10.1109/ICORR.2017.8009397

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

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