research-article

Stroke rehabilitation with a sensing surface

Authors:

Cati Boulanger,

Adam Boulanger,

Lilian de Greef,

Russell Transue,

Steven BathicheAuthors Info & Claims

CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1243 - 1246

https://doi.org/10.1145/2470654.2466160

Published: 27 April 2013 Publication History

Abstract

This paper presents a new sensing and interaction environment for post-stroke and upper extremity limb rehabilitation. The device is a combination of camera-based multitouch sensing and a supporting therapeutic software application that advances the treatment, provides feedback, and records a user's progress. The image-based analysis of hand position provided by a Microsoft Surface is used as an input into a tabletop game environment. Tailored image analysis algorithms assess rehabilitative hand movements. Visual feedback is provided in a game context. Experiments were conducted in a sub-acute rehabilitation center. Preliminary user studies with a stroke-afflicted population determined essential design criteria. Hand and wrist sensing, as well as the goals of the supporting game environment, engage therapeutic flexion and extension as defined by consulted physicians. Participants valued personalization of the activity, novelty, reward and the ability to work at their own pace in an otherwise repetitive therapeutic task. A "character" - game element personifying the participant's movement - was uniquely motivating relative to the media available in the typical therapeutic routine.

References

[1]

Adamovich, S. V. et al (2011). A virtual reality-based exercise system for hand rehabilitation post-stroke. Prescence: Teleop. Virtual Environ. 14, 2, 161--174.

Digital Library

[2]

Alankus, G. et al. (2010). Stroke therapy through motionbased games: a case study. In ACM SIGACCESS conference on Computers and accessibility. Pp. 219--226.

Digital Library

[3]

Back-y-Rita et al. (2002) Computer-assisted motivating rehabilitation for institutional, home, and educational late stroke programs. Topics Stroke Rehab. 8, 4, 1--10.

[4]

Chedoke Arm and Hand Activity Inventory (2004).

[5]

Chen, C. C. and Bode, R. K. (2011). Factors influencing therapists' decision-making in the acceptance of new technology devices in stroke rehabilitation. Am J Phys Med Rehabil. 90(5):415--425.

[6]

Hecht, D., Reiner, M., and Karni, A. (2008). Multisensory enhancement: gains in choice and in simple response times. Exp Brain Res, 189(2):133--143.

[7]

Internet Stroke Center (2012).

[8]

Krebs, H. I. et al. (2008). A comparison of functional and impairment-based robotic training in severe to moderate chronic stroke: a pilot study. NeuroRehabilitation, 23(1):81--87.

[9]

Lewis, G. N., Woods, C., Rosie, J. A. and McPherson, K. M. (2011). Virtual reality games for rehabilitation of people with stroke: Perspectives from the users. Disabil Rehabil Assist Technol 6(5):453--463.

[10]

Molholm, S. et al. (2004). Multisensory visual-auditory object recognition in humans: a highdensity electrical mapping study. Cereb Cortex, 14(4):452--465.

[11]

National Stroke Association (2006). New survey emphasizes need for more, better care after stroke.

[12]

Pizzamiglio, L. et al. (2005). Separate neural systems for processing action- or non-action-related sounds. Neuroimage, 24(3):852--861.

[13]

Stein, J. (2009). e100 neurorobotic system. Expert Rev Med Devices, 6(1):15--19.

[14]

Sugar, T. G. et al. (2007) Design and control of RUPERT: a device for robotic upper extremity repetitive therapy, IEEE Transactions on neural systems and rehabilitation engineering, 15(3):336--46.

[15]

Teder-sälejärvi, W. A., Di Russo, F., McDonald, J. J., and Hillyard, S. A. (2005). Effects of spatial congruity on audio-visual multimodal integration. J Cogn Neurosci, 17(9):1396--14.

Digital Library

[16]

World Health Organization {WHO}. (2002). Innovative Care for Chronic Conditions: Building Blocks For Action. Retrieved from http://www.who.int/chp/knowledge/publications/icccreport/en/

Cited By

Yang ZXu XYao BRogers EZhang SIntille SShara NGao GWang D(2024)Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older AdultsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596258:2(1-35)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659625
Sanaei MGilbert SDorneich M(2022)The Roles of Instructional Agents in Human-Agent Interaction Within Serious GamesHCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games10.1007/978-3-031-22131-6_47(642-655)Online publication date: 25-Nov-2022
https://doi.org/10.1007/978-3-031-22131-6_47
Mégard CBouchigny SPouplin SBonnyaud CBertholier LGoulamhoussen RFoulon PRoche NBarbot F(2022)Including Grip Strength Activities into Tabletop Training EnvironmentsSmart Multimedia10.1007/978-3-031-22061-6_19(261-271)Online publication date: 25-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-22061-6_19
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Index Terms

Stroke rehabilitation with a sensing surface
1. Human-centered computing

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cover image ACM Conferences

CHI '13: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2013

3550 pages

ISBN:9781450318990

DOI:10.1145/2470654

General Chair:
Wendy E. Mackay
INRIA
,
Program Chairs:
Stephen Brewster
Glasgow University
,
Susanne Bødker
University of Aarhus

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

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Published: 27 April 2013

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CHI '13: CHI Conference on Human Factors in Computing Systems

April 27 - May 2, 2013

Paris, France

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CHI '13 Paper Acceptance Rate 392 of 1,963 submissions, 20%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Yang ZXu XYao BRogers EZhang SIntille SShara NGao GWang D(2024)Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older AdultsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36596258:2(1-35)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659625
Sanaei MGilbert SDorneich M(2022)The Roles of Instructional Agents in Human-Agent Interaction Within Serious GamesHCI International 2022 - Late Breaking Papers. Interaction in New Media, Learning and Games10.1007/978-3-031-22131-6_47(642-655)Online publication date: 25-Nov-2022
https://doi.org/10.1007/978-3-031-22131-6_47
Mégard CBouchigny SPouplin SBonnyaud CBertholier LGoulamhoussen RFoulon PRoche NBarbot F(2022)Including Grip Strength Activities into Tabletop Training EnvironmentsSmart Multimedia10.1007/978-3-031-22061-6_19(261-271)Online publication date: 25-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-22061-6_19
Lewis BVenkatasubramanian KKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)“I...Got my Nose-Print. But it Wasn’t Accurate”: How People with Upper Extremity Impairment Authenticate on their Personal Computing DevicesProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445070(1-14)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445070
Koutsiana ELadakis IFotopoulos DChytas AKilintzis VChouvarda I(2020)Serious Gaming Technology in Upper Extremity Rehabilitation: Scoping ReviewJMIR Serious Games10.2196/190718:4(e19071)Online publication date: 11-Dec-2020
https://doi.org/10.2196/19071
Zou FChang DSong F(2020)An APP Design for Stroke RehabilitationAdvances in Human Factors and Ergonomics in Healthcare and Medical Devices10.1007/978-3-030-50838-8_40(289-296)Online publication date: 1-Jul-2020
https://doi.org/10.1007/978-3-030-50838-8_40
Megard CBouchigny SMartin AGoulamhoussen RBertholier LFoulon PPouplin SBonnyaud CRoche NGelineau ABarbot FBrewster SFitzpatrick GCox AKostakos V(2019)ErgotactExtended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290607.3312920(1-6)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290607.3312920
Chang WSu JChen LHsu CLin CYang TChen MOu Y(2019)BodyTracker: A Deep Learning Based 3D Limb Trajectory Tracking System for Rehabilitation2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)10.1109/GCCE46687.2019.9015359(383-384)Online publication date: Oct-2019
https://doi.org/10.1109/GCCE46687.2019.9015359
Fazeli HVenkatesh SPeng Q(2018)A Virtual Environment for Hand Motion AnalysisProcedia CIRP10.1016/j.procir.2018.09.06078(127-132)Online publication date: 2018
https://doi.org/10.1016/j.procir.2018.09.060
Alsinglawi BAlnajjar FMubin ONovoa M(2018)A Framework for Home-Based Stroke Rehabilitation Using Interactive Games and Augmented Reality FeedbackConverging Clinical and Engineering Research on Neurorehabilitation III10.1007/978-3-030-01845-0_50(252-255)Online publication date: 16-Oct-2018
https://doi.org/10.1007/978-3-030-01845-0_50
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