abstract

Public Access

Behavior Adaptation for Robot-assisted Neurorehabilitation

Authors:

Laurel D. RiekAuthors Info & Claims

HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

Pages 565 - 567

https://doi.org/10.1145/3434074.3446359

Published: 08 March 2021 Publication History

Abstract

11% of adults report experiencing cognitive decline which can impact memory, behavior, and physical abilities. Robots have great potential to support people with cognitive impairments, their caregivers, and clinicians by facilitating treatments such as cognitive neurorehabilitation. Personalizing these treatments to individual preferences and goals is critical to improving engagement and adherence, which helps improve treatment efficacy. In our work, we explore the efficacy of robot-assisted neurorehabilitation and aim to enable robots to adapt their behavior to people with cognitive impairments, a unique population whose preferences and abilities may change dramatically during treatment. Our work aims to enable more engaging and personalized interactions between people and robots, which can profoundly impact robot-assisted treatment, how people receive care, and improve their everyday lives.

References

[1]

A. Barman, A. Chatterjee, and R. Bhide. Cognitive impairment and rehabilitation strategies after traumatic brain injury. Indian journal of psychological medicine, 38(3):172, 2016.

[2]

H. Chen, H. W. Park, and C. Breazeal. Teaching and learning with children: Impact of reciprocal peer learning with a social robot on children's learning and emotive engagement. Computers & Education, 150:103836, 2020.

Digital Library

[3]

G. Connie, A. Bouzida, R. Oncy-Avila, S. Moharana, and L. D. Riek. Taking an (embodied) cue from community health: Designing dementia caregiver support technology to advance health equity. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, CHI '21, pages 1--24, 2021.

[4]

J. M. Fleming, D. Shum, J. Strong, and S. Lightbody. Prospective memory rehabilitation for adults with traumatic brain injury: A compensatory training programme. Brain Injury, 19(1):1--10, 2005.

[5]

E. Frank Lopresti, A. Mihailidis, and N. Kirsch. Assistive technology for cognitive rehabilitation: State of the art. Neuropsychological rehabilitation, 14(1--2):5--39, 2004.

[6]

M. Huckans, L. Hutson, E. Twamley, A. Jak, J. Kaye, and D. Storzbach. Efficacy of cognitive rehabilitation therapies for mild cognitive impairment (mci) in older adults: working toward a theoretical model and evidence-based interventions. Neuropsychology review, 23(1):63--80, 2013.

[7]

R. Kittmann, T. Fröhlich, J. Schäfer, U. Reiser, F. Weißhardt, and A. Haug. Let me introduce myself: I am care-o-bot 4, a gentleman robot. Mensch und computer 2015--proceedings, 2015.

[8]

A. Kubota, E. I. Peterson, V. Rajendren, H. Kress-Gazit, and L. D. Riek. Jessie: Synthesizing social robot behaviors for personalized neurorehabilitation and beyond. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pages 121--130, 2020.

Digital Library

[9]

A. Kubota and L. D. Riek. Methods for robot behavior adaptation for cognitive neurorehabilitation. Annual Review of Control, Robotics, and Autonomous Systems, 5, 2021.

[10]

H. R. Lee and L. D. Riek. Reframing assistive robots to promote successful aging. ACM Transactions on Human-Robot Interaction (THRI), 7(1):1--23, 2018.

[11]

I. Leite, C. Martinho, and A. Paiva. Social robots for long-term interaction: a survey. International Journal of Social Robotics, 5(2):291--308, 2013.

[12]

S. Moharana, A. E. Panduro, H. R. Lee, and L. D. Riek. Robots for joy, robots for sorrow: community based robot design for dementia caregivers. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pages 458--467. IEEE, 2019.

[13]

W. Moyle, C. J. Jones, J. E. Murfield, L. Thalib, E. R. Beattie, D. K. Shum, S. T. O'Dwyer, M. C. Mervin, and B. M. Draper. Use of a robotic seal as a therapeutic tool to improve dementia symptoms: a cluster-randomized controlled trial. Journal of the American Medical Directors Association, 18(9):766--773, 2017.

[14]

H. W. Park, I. Grover, S. Spaulding, L. Gomez, and C. Breazeal. A model-free affective reinforcement learning approach to personalization of an autonomous social robot companion for early literacy education. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 687--694, 2019.

Digital Library

[15]

O. Pino, G. Palestra, R. Trevino, and B. De Carolis. The humanoid robot nao as trainer in a memory program for elderly people with mild cognitive impairment. International Journal of Social Robotics, 12(1):21--33, 2020.

[16]

L. D. Riek. Healthcare robotics. Communications of the ACM, 60(11):68--78, 2017.

Digital Library

[17]

H. Ritschel and E. André. Real-time robot personality adaptation based on reinforcement learning and social signals. In Proceedings of the companion of the 2017 acm/ieee international conference on human-robot interaction, pages 265--266, 2017.

Digital Library

[18]

E. Senft, P. Baxter, J. Kennedy, and T. Belpaeme. Sparc: Supervised progressively autonomous robot competencies. In International Conference on Social Robotics, pages 603--612. Springer, 2015.

[19]

A. Tapus, C. Tapus, and M. Mataric. The role of physical embodiment of a therapist robot for individuals with cognitive impairments. In RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication, pages 103--107. IEEE, 2009.

[20]

A. Tapus, C. ??pu?, and M. J. Matarić. User-robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intelligent Service Robotics, 1(2):169, 2008.

[21]

K. Tsiakas, M. Abujelala, and F. Makedon. Task engagement as personalization feedback for socially-assistive robots and cognitive training. Technologies, 6(2):49, 2018.

[22]

Z. Wang, M. K. Singh, C. Zhang, L. D. Riek, and K. Chaudhuri. Stochastic multi-player bandit learning from player-dependent feedback. In ICML Workshop on Real World Experiment Design and Active Learning, 2020.

[23]

B. Woodworth, F. Ferrari, T. E. Zosa, and L. D. Riek. Preference learning in assistive robotics: Observational repeated inverse reinforcement learning. In Machine Learning for Healthcare Conference, pages 420--439, 2018.

[24]

Y. Zhai. A call for addressing barriers to telemedicine: health disparities during the covid-19 pandemic. Psychotherapy and Psychosomatics, page 1, 2020.

Cited By

Zhao YLi LHe XYin SZhou YMarquez-Chin CYang WRao JXiang WLiu BLi J(2023)Psychodynamic-based virtual reality cognitive training system with personalized emotional arousal elements for mild cognitive impairment patientsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2023.107779241:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.cmpb.2023.107779
Card A(2022)The biopsychosociotechnical model: a systems-based framework for human-centered health improvementHealth Systems10.1080/20476965.2022.202958412:4(387-407)Online publication date: 30-Jan-2022
https://doi.org/10.1080/20476965.2022.2029584
Guan CBouzida AOncy-avila RMoharana SRiek LKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Taking an (Embodied) Cue From Community Health: Designing Dementia Caregiver Support Technology to Advance Health EquityProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445559(1-16)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445559

Index Terms

Behavior Adaptation for Robot-assisted Neurorehabilitation
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Robotics
      1. Robotic control

Recommendations

Meeting Requirements of Older Users? Robot Prototype Trials in a Home-like Environment
Proceedings of the 8th International Conference on Universal Access in Human-Computer Interaction. Aging and Assistive Environments - Volume 8515

A prototype of an assistive robot for older people was tested in three different countries in life-like lab settings. A sample of potential older users with different grades and types of age-related impairments completed a sequence of tasks with the ...
Leading a Person Using Ethologically Inspired Autonomous Robot Behavior
HRI'15 Extended Abstracts: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts

This study considered a leading behavior of a robot. To lead a person when the person's attention is initially elsewhere, the robot's behavior needs to be designed so that it seeks the person's attention and seamlessly brings him or her to the target ...
An Exploratory Study on People's Intuitive Understanding of Expressive Robot Behavior
HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

Robots are anticipated to communicate with humans in our social context. Using nonverbal communication by robots increases communication comprehension because humans intuitively understand such behavior due to their experience of human-human interaction. ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

HRI '21 Companion: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction

March 2021

756 pages

ISBN:9781450382908

DOI:10.1145/3434074

General Chairs:
Cindy Bethel
Mississippi State University, USA
,
Ana Paiva
INESC-ID, IST, University of Lisbon, Portugal & Radcliffe Institute for Advanced Study, Harvard University, USA
,
Program Chairs:
Elizabeth Broadbent
University of Auckland, New Zealand
,
David Feil-Seifer
University of Nevada Reno, USA
,
Daniel Szafir
University of Colorado Boulder, USA

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2021

Check for updates

Author Tags

Qualifiers

Abstract

Funding Sources

National Science Foundation

Conference

HRI '21

Sponsor:

HRI '21: ACM/IEEE International Conference on Human-Robot Interaction

March 8 - 11, 2021

CO, Boulder, USA

Acceptance Rates

Overall Acceptance Rate 192 of 519 submissions, 37%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
307
Total Downloads

Downloads (Last 12 months)99
Downloads (Last 6 weeks)22

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Zhao YLi LHe XYin SZhou YMarquez-Chin CYang WRao JXiang WLiu BLi J(2023)Psychodynamic-based virtual reality cognitive training system with personalized emotional arousal elements for mild cognitive impairment patientsComputer Methods and Programs in Biomedicine10.1016/j.cmpb.2023.107779241:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.cmpb.2023.107779
Card A(2022)The biopsychosociotechnical model: a systems-based framework for human-centered health improvementHealth Systems10.1080/20476965.2022.202958412:4(387-407)Online publication date: 30-Jan-2022
https://doi.org/10.1080/20476965.2022.2029584
Guan CBouzida AOncy-avila RMoharana SRiek LKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Taking an (Embodied) Cue From Community Health: Designing Dementia Caregiver Support Technology to Advance Health EquityProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445559(1-16)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445559

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents