title: Grasps recognition and evaluation of stroke patients for supporting rehabilitation therapy.
abstract: 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.
authors.affiliation: Adaptive Systems Research Group at the School of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, UK.
authors.forename: Beatriz
authors.identifier: 0000-0002-0831-9003
authors.initials: B
authors.lastname: Leon
authors.affiliation: Adaptive Systems Research Group at the School of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, UK.
authors.forename: Angelo
authors.identifier: 0000-0003-4009-9984
authors.initials: A
authors.lastname: Basteris
authors.affiliation: IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Roma, Italy.
authors.forename: Francesco
authors.identifier: 0000-0002-5570-8490
authors.initials: F
authors.lastname: Infarinato
authors.affiliation: IRCCS San Raffaele Pisana, Via di Val Cannuta 247, 00166 Roma, Italy.
authors.forename: Patrizio
authors.initials: P
authors.lastname: Sale
authors.affiliation: Roessingh Research and Development, Roessinghsbleekweg 33b, 7522 AH Enschede, The Netherlands.
authors.forename: Sharon
authors.initials: S
authors.lastname: Nijenhuis
authors.affiliation: Roessingh Research and Development, Roessinghsbleekweg 33b, 7522 AH Enschede, The Netherlands.
authors.forename: Gerdienke
authors.initials: G
authors.lastname: Prange
authors.affiliation: Adaptive Systems Research Group at the School of Computer Science, University of Hertfordshire, Hatfield, Hertfordshire AL10 9AB, UK.
authors.forename: Farshid
authors.initials: F
authors.lastname: Amirabdollahian
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D005385:Fingers / Q000502:physiology; D005868:Gestures; D018737:Hand Strength / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009043:Motor Activity / Q000502:physiology; D011187:Posture; D011597:Psychomotor Performance; D021641:Recognition, Psychology; D012044:Regression Analysis; D020521:Stroke / Q000503:physiopathology*; D000071939:Stroke Rehabilitation; D013997:Time Factors