Repository logo
 
Publication

Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment

2020Conference object Open access
Simple item page
dc.contributor.authorLee, Min Hun
dc.contributor.authorSiewiorek, Daniel P.
dc.contributor.authorSmailagic, Asim
dc.contributor.authorBernardino, Alexandre
dc.contributor.authorBermúdez i Badia, Sergi
dc.date.accessioned2022-07-20T14:08:54Z
dc.date.available2022-07-20T14:08:54Z
dc.date.issued2020
dc.description.abstractClinical decision support systems have the potential to improve work flows of experts in practice (e.g. therapist’s evidence-based rehabilitation assessment). However, the adoption of these systems is challenging, and the gains of these systems have not fully demonstrated yet. In this paper, we identified the needs of therapists to assess patient’s functional abilities (e.g. alternative perspectives with quantitative information on patient’s exercise motions). As a result, we co-designed and developed an intelligent decision support system that automatically identifies salient features of assessment using reinforcement learning to assess the quality of motion and generate patient-specific analysis. We evaluated this system with seven therapists using the dataset from 15 patients performing three exercises. The results show that therapists have higher usage intent on our system than a traditional system without patient-specific analysis (𝑝 < 0.05). While presenting richer information (𝑝 < 0.10), our system significantly reduces therapists’ effort on assessment (𝑝 < 0.10) and improves their agreement on assessment from 0.66 to 0.71 F1-scores (𝑝 < 0.01). This work discusses the importance of human centered design and development of a machine learning-based decision support system that presents contextually relevant information and salient explanations on its prediction for better adoption in practice.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationLee, M. H., Siewiorek, D. P., Smailagic, A., Bernardino, A., & Bermúdez i Badia, S. (2020). Co-design and evaluation of an intelligent decision support system for stroke rehabilitation assessment. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1-27.pt_PT
dc.identifier.doi10.1145/3415227pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/4436
dc.language.isoengpt_PT
dc.publisherACMpt_PT
dc.relationThe AHA project aims to develop a robotic assistance plataform for supporting healthy lifestyle and sustain active aging.
dc.relationLaboratory of Robotics and Engineering Systems
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectHuman-centered computingpt_PT
dc.subjectInteractive systems and toolspt_PT
dc.subjectUser studiespt_PT
dc.subjectApplied computingpt_PT
dc.subjectHealth care information systemspt_PT
dc.subjectComputing methodologiespt_PT
dc.subjectReinforcement learningpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleCo-design and evaluation of an intelligent decision support system for stroke rehabilitation assessmentpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleThe AHA project aims to develop a robotic assistance plataform for supporting healthy lifestyle and sustain active aging.
oaire.awardTitleLaboratory of Robotics and Engineering Systems
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F113694%2F2015/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50009%2F2020/PT
oaire.citation.endPage27pt_PT
oaire.citation.issueCSCW2pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleProceedings of the ACM on Human-Computer Interactionpt_PT
oaire.citation.volume4pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameBermúdez i Badia
person.givenNameSergi
person.identifier239789
person.identifier.ciencia-idCA17-5E88-2B37
person.identifier.orcid0000-0003-4452-0414
person.identifier.ridC-8681-2018
person.identifier.scopus-author-id6506360007
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationef8f1e3b-3c09-4817-80d0-d96aa88051a2
relation.isAuthorOfPublication.latestForDiscoveryef8f1e3b-3c09-4817-80d0-d96aa88051a2
relation.isProjectOfPublicationa7d87d10-aada-456c-8552-7896d28a2552
relation.isProjectOfPublicationc0352e81-99e6-4923-99ed-074df09e4db0
relation.isProjectOfPublication.latestForDiscoverya7d87d10-aada-456c-8552-7896d28a2552

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Co-Design and Evaluation of an Intelligent Decision Support System for Stroke Rehabilitation Assessment.pdf
Size:
4.03 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Publicações em Atas de Congressos/Conferências, etc.