Advancing NASA-TLX: Automatic User Interaction Analysis for Workload Evaluation in XR Scenarios

UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleGEM 2024es_ES
UDC.departamentoEnxeñaría de Computadoreses_ES
UDC.endPage6es_ES
UDC.grupoInvGrupo de Tecnoloxía Electrónica e Comunicacións (GTEC)es_ES
UDC.issue2024es_ES
UDC.startPage1es_ES
dc.contributor.authorVidal-Balea, Aida
dc.contributor.authorFraga-Lamas, Paula
dc.contributor.authorFernández-Caramés, Tiago M.
dc.date.accessioned2024-10-31T09:12:32Z
dc.descriptionPresented at: 2024 IEEE Gaming, Entertainment, and Media Conference, GEM 2024, Turin, 5 June 2024 through 7 June 2024es_ES
dc.descriptionThis version of the paper has been accepted for publication.es_ES
dc.description.abstract[Abstract]: Calculating the effort required to complete a task has always been somewhat difficult, as it depends on each person and becomes very subjective. For this reason, different methodologies were developed to try to standardize these procedures. This article addresses some of the problems that arise when applying NASA-Task Load Index (NASA-TLX), a methodology to calculate the mental workload of tasks performed in industrial environments. In addition, an improvement of this methodology is proposed to adapt it to the new times and to emerging Extended Reality (XR) technologies. Finally, a system is proposed for automatic collection of user performance metrics, providing an autonomous method that collects this information and does not depend on the users' willingness to fill in a feedback questionnaire.es_ES
dc.description.sponsorshipThis work has been funded by grant PID2020-118857RA-100 (ORBALLO) funded by MCIN/AEI/10.13039/501100011033 and by Centro Mixto de Investigación UDC-NAVANTIA (IN853C 2022/01), funded by GAIN (Xunta de Galicia) and ERDF Galicia 2021-2027.es_ES
dc.description.sponsorshipXunta de Galicia; IN853C 2022/01es_ES
dc.identifier.citationA. Vidal-Balea, P. Fraga-Lamas and T. M. Fernández-Caramés, "Advancing NASA-TLX: Automatic User Interaction Analysis for Workload Evaluation in XR Scenarios," 2024 IEEE Gaming, Entertainment, and Media Conference (GEM), Turin, Italy, 2024, pp. 1-6, doi: 10.1109/GEM61861.2024.10585425es_ES
dc.identifier.doi10.1109/GEM61861.2024.10585425
dc.identifier.isbn9798350374537
dc.identifier.urihttp://hdl.handle.net/2183/39901
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineers Inc.es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118857RA-100/ES/EDGE COMPUTING OPORTUNISTA BASADO EN DISPOSITIVOS LOT MÓVILES Y DE BAJA POTENCIA (ORBALLO)es_ES
dc.relation.urihttps://doi.org/10.1109/GEM61861.2024.10585425es_ES
dc.rights© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published paper is available online at: https://doi.org/10.1109/GEM61861.2024.10585425es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectAugmented realityes_ES
dc.subjectExtended realityes_ES
dc.subjectMental workloades_ES
dc.subjectMicrosoft HoloLenses_ES
dc.subjectMixed realityes_ES
dc.subjectNASA-TLXes_ES
dc.subjectXR NASA-TLXes_ES
dc.titleAdvancing NASA-TLX: Automatic User Interaction Analysis for Workload Evaluation in XR Scenarioses_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationcaa923d2-cf88-405e-9025-759d06cf3799
relation.isAuthorOfPublication79dbfabd-7261-41ff-9667-2f774d5f341e
relation.isAuthorOfPublication.latestForDiscoverycaa923d2-cf88-405e-9025-759d06cf3799

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