Automatic outcome in manual dexterity assessment using colour segmentation and nearest neighbour classifier

dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Robótica (Robotics Lab)es
dc.contributor.authorOña Simbaña, Edwin Daniel
dc.contributor.authorSánchez Herrera, Patricia
dc.contributor.authorCuesta Gomez, Alicia
dc.contributor.authorMartínez de la Casa Díaz, Santiago
dc.contributor.authorJardón Huete, Alberto
dc.contributor.authorBalaguer Bernaldo de Quirós, Carlos
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderComunidad de Madrides
dc.descriptionThis article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeingen
dc.description.abstractObjective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson's disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes' displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user's performance.en
dc.identifier.bibliographicCitationOña, E., Sánchez-Herrera, P., Cuesta-Gómez, A., Martinez, S., Jardón, A., & Balaguer, C. (2018). Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier. Sensors 18 (9), p.
dc.relation.projectIDGobierno de España. DPI2013-47944-C4-1-Res
dc.relation.projectIDComunidad de Madrid. S2013/MIT-2748es
dc.rights© 2018 by the authors.en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.subject.ecienciaIngeniería Mecánicaes
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherColour segmentationen
dc.subject.otherAutomatic countingen
dc.subject.otherNn-based classifieren
dc.subject.otherManual dexterityen
dc.subject.otherNeurological rehabilitationen
dc.titleAutomatic outcome in manual dexterity assessment using colour segmentation and nearest neighbour classifieren
dc.typeresearch article*
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