Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Objective 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. T...

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Main Authors: Edwin Daniel Oña, Patricia Sánchez-Herrera, Alicia Cuesta-Gómez, Santiago Martinez, Alberto Jardón, Carlos Balaguer
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/18/9/2876
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author Edwin Daniel Oña
Patricia Sánchez-Herrera
Alicia Cuesta-Gómez
Santiago Martinez
Alberto Jardón
Carlos Balaguer
author_facet Edwin Daniel Oña
Patricia Sánchez-Herrera
Alicia Cuesta-Gómez
Santiago Martinez
Alberto Jardón
Carlos Balaguer
author_sort Edwin Daniel Oña
collection DOAJ
description Objective 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.
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spelling doaj.art-5992ff5897784e4891acbc0b1d8b60482022-12-22T03:45:26ZengMDPI AGSensors1424-82202018-08-01189287610.3390/s18092876s18092876Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour ClassifierEdwin Daniel Oña0Patricia Sánchez-Herrera1Alicia Cuesta-Gómez2Santiago Martinez3Alberto Jardón4Carlos Balaguer5Department of Systems Engineering and Automation, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, SpainDepartment of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Rey Juan Carlos University, Avda. de atenas s/n, 28922 Alcorcón, SpainDepartment of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Rey Juan Carlos University, Avda. de atenas s/n, 28922 Alcorcón, SpainDepartment of Systems Engineering and Automation, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, SpainDepartment of Systems Engineering and Automation, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, SpainDepartment of Systems Engineering and Automation, University Carlos III of Madrid, Avda. de la Universidad 30, 28911 Leganés, SpainObjective 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.http://www.mdpi.com/1424-8220/18/9/2876colour segmentationCIELabautomatic countingNN-based classifiermanual dexterityassessmentneurological rehabilitation
spellingShingle Edwin Daniel Oña
Patricia Sánchez-Herrera
Alicia Cuesta-Gómez
Santiago Martinez
Alberto Jardón
Carlos Balaguer
Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier
Sensors
colour segmentation
CIELab
automatic counting
NN-based classifier
manual dexterity
assessment
neurological rehabilitation
title Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier
title_full Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier
title_fullStr Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier
title_full_unstemmed Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier
title_short Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier
title_sort automatic outcome in manual dexterity assessment using colour segmentation and nearest neighbour classifier
topic colour segmentation
CIELab
automatic counting
NN-based classifier
manual dexterity
assessment
neurological rehabilitation
url http://www.mdpi.com/1424-8220/18/9/2876
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