Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy
Stroke survivors often suffer impairments on their wrist and hand. Robot-mediated rehabilitation techniques have been proposed as a way to enhance conventional therapy, based on intensive repeated movements. Amongst the set of activities of daily living, grasping is one of the most recurrent. Our ai...
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doaj-3e58415edb4a4f83bddef979795109482020-11-24T23:11:36ZengHindawi LimitedBioMed Research International2314-61332314-61412014-01-01201410.1155/2014/318016318016Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation TherapyBeatriz Leon0Angelo Basteris1Francesco Infarinato2Patrizio Sale3Sharon Nijenhuis4Gerdienke Prange5Farshid Amirabdollahian6Adaptive Systems Research Group at the School of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, UKAdaptive Systems Research Group at the School of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, UKIRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Roma, ItalyIRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Roma, ItalyRoessingh Research and Development, Roessinghsbleekweg 33b, 7522 AH Enschede, The NetherlandsRoessingh Research and Development, Roessinghsbleekweg 33b, 7522 AH Enschede, The NetherlandsAdaptive Systems Research Group at the School of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, UKStroke survivors often suffer impairments on their wrist and hand. Robot-mediated rehabilitation techniques have been proposed as a way to enhance conventional therapy, based on intensive repeated movements. Amongst the set of activities of daily living, grasping is one of the most recurrent. Our aim is to incorporate the detection of grasps in the machine-mediated rehabilitation framework so that they can be incorporated into interactive therapeutic games. In this study, we developed and tested a method based on support vector machines for recognizing various grasp postures wearing a passive exoskeleton for hand and wrist rehabilitation after stroke. The experiment was conducted with ten healthy subjects and eight stroke patients performing the grasping gestures. The method was tested in terms of accuracy and robustness with respect to intersubjects’ variability and differences between different grasps. Our results show reliable recognition while also indicating that the recognition accuracy can be used to assess the patients’ ability to consistently repeat the gestures. Additionally, a grasp quality measure was proposed to measure the capabilities of the stroke patients to perform grasp postures in a similar way than healthy people. These two measures can be potentially used as complementary measures to other upper limb motion tests.http://dx.doi.org/10.1155/2014/318016 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Beatriz Leon Angelo Basteris Francesco Infarinato Patrizio Sale Sharon Nijenhuis Gerdienke Prange Farshid Amirabdollahian |
spellingShingle |
Beatriz Leon Angelo Basteris Francesco Infarinato Patrizio Sale Sharon Nijenhuis Gerdienke Prange Farshid Amirabdollahian Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy BioMed Research International |
author_facet |
Beatriz Leon Angelo Basteris Francesco Infarinato Patrizio Sale Sharon Nijenhuis Gerdienke Prange Farshid Amirabdollahian |
author_sort |
Beatriz Leon |
title |
Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy |
title_short |
Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy |
title_full |
Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy |
title_fullStr |
Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy |
title_full_unstemmed |
Grasps Recognition and Evaluation of Stroke Patients for Supporting Rehabilitation Therapy |
title_sort |
grasps recognition and evaluation of stroke patients for supporting rehabilitation therapy |
publisher |
Hindawi Limited |
series |
BioMed Research International |
issn |
2314-6133 2314-6141 |
publishDate |
2014-01-01 |
description |
Stroke survivors often suffer impairments on their wrist and hand. Robot-mediated rehabilitation techniques have been proposed as a way to enhance conventional therapy, based on intensive repeated movements. Amongst the set of activities of daily living, grasping is one of the most recurrent. Our aim is to incorporate the detection of grasps in the machine-mediated rehabilitation framework so that they can be incorporated into interactive therapeutic games. In this study, we developed and tested a method based on support vector machines for recognizing various grasp postures wearing a passive exoskeleton for hand and wrist rehabilitation after stroke. The experiment was conducted with ten healthy subjects and eight stroke patients performing the grasping gestures. The method was tested in terms of accuracy and robustness with respect to intersubjects’ variability and differences between different grasps. Our results show reliable recognition while also indicating that the recognition accuracy can be used to assess the patients’ ability to consistently repeat the gestures. Additionally, a grasp quality measure was proposed to measure the capabilities of the stroke patients to perform grasp postures in a similar way than healthy people. These two measures can be potentially used as complementary measures to other upper limb motion tests. |
url |
http://dx.doi.org/10.1155/2014/318016 |
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