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dc.contributor.authorPadilla Magaña, Jesús Fernando
dc.contributor.authorPeña Pitarch, Esteve
dc.contributor.authorSánchez Suarez, Isahi
dc.contributor.authorTicó Falguera, Neus
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Automàtica, Robòtica i Visió
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica
dc.date.accessioned2022-05-09T14:18:43Z
dc.date.available2022-05-09T14:18:43Z
dc.date.issued2022-05-01
dc.identifier.citationPadilla-Magana, J. [et al.]. Hand motion analysis during the execution of the action research arm test using multiple sensors. "Sensors", 1 Maig 2022, vol. 22, núm. 9, p. 3276:1-3276:20.
dc.identifier.issn1424-3210
dc.identifier.urihttp://hdl.handle.net/2117/367109
dc.description.abstractThe Action Research Arm Test (ARAT) is a standardized outcome measure that can be improved by integrating sensors for hand motion analysis. The purpose of this study is to measure the flexion angle of the finger joints and fingertip forces during the performance of three subscales (Grasp, Grip, and Pinch) of the ARAT, using a data glove (CyberGlove II®) and five force-sensing resistors (FSRs) simultaneously. An experimental study was carried out with 25 healthy subjects (right-handed). The results showed that the mean flexion angles of the finger joints required to perform the 16 activities were Thumb (Carpometacarpal Joint (CMC) 28.56°, Metacarpophalangeal Joint (MCP) 26.84°, and Interphalangeal Joint (IP) 13.23°), Index (MCP 46.18°, Index Proximal Interphalangeal Joint (PIP) 38.89°), Middle (MCP 47.5°, PIP 42.62°), Ring (MCP 44.09°, PIP 39.22°), and Little (MCP 31.50°, PIP 22.10°). The averaged fingertip force exerted in the Grasp Subscale was 8.2 N, in Grip subscale 6.61 N and Pinch subscale 3.89 N. These results suggest that the integration of multiple sensors during the performance of the ARAT has clinical relevance, allowing therapists and other health professionals to perform a more sensitive, objective, and quantitative assessment of the hand function.
dc.language.isoeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights© MDPI
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Enginyeria mecànica
dc.subject.lcshHand -- Wounds and injuries
dc.subject.lcshMedical rehabilitation
dc.subject.lcshDetectors
dc.subject.otherFinger joints
dc.subject.otherFlexion angle
dc.subject.otherFingertip force
dc.subject.otherAction research arm test
dc.subject.otherHand
dc.titleHand motion analysis during the execution of the action research arm test using multiple sensors
dc.typeArticle
dc.subject.lemacMans -- Ferides i lesions
dc.subject.lemacRehabilitació mèdica
dc.subject.lemacDetectors
dc.contributor.groupUniversitat Politècnica de Catalunya. SIR - Service and Industrial Robotics
dc.identifier.doi10.3390/s22093276
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/22/9/3276
dc.rights.accessOpen Access
local.identifier.drac33221509
dc.description.versionPostprint (published version)
local.citation.authorPadilla-Magana, J.; Peña-Pitarch, E.; Sánchez, I.; Ticó, N.
local.citation.publicationNameSensors
local.citation.volume22
local.citation.number9
local.citation.startingPage3276:1
local.citation.endingPage3276:20


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