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An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises

2020Conference object Open access
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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.accessioned2022-07-21T09:51:31Z
dc.date.available2022-07-21T09:51:31Z
dc.date.issued2020
dc.description.abstractTechnology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient’s quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to pa tients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learn ing (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected thera pist’s knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.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, July). An exploratory study on techniques for quantitative assessment of stroke rehabilitation exercises. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 303-307).pt_PT
dc.identifier.doi10.1145/3340631.3394872pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/4440
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.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/pt_PT
dc.subjectHuman-AI interactionpt_PT
dc.subjectExplainable AIpt_PT
dc.subjectDecision support systemspt_PT
dc.subjectHuman activity recognitionpt_PT
dc.subjectStroke rehabilitation assessmentpt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleAn exploratory study on techniques for quantitative assessment of stroke rehabilitation exercisespt_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.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F113694%2F2015/PT
oaire.citation.endPage307pt_PT
oaire.citation.startPage303pt_PT
oaire.citation.titleProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalizationpt_PT
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.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
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relation.isProjectOfPublication.latestForDiscoverya7d87d10-aada-456c-8552-7896d28a2552

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