Automation of motor dexterity assessment
File(s)
Submitted version
Author(s)
Heyer, Patrick
Castrejon, Luis R
Orihuela-Espina, Felipe
Sucar, Luis Enrique
Type
Conference Paper
Abstract
Motor dexterity assessment is regularly performed in rehabilitation wards to establish patient status and automatization for such routinary task is sought. A system for automatizing the assessment of motor dexterity based on the Fugl-Meyer scale and with loose restrictions on sensing technologies is presented. The system consists of two main elements: 1) A data representation that abstracts the low level information obtained from a variety of sensors, into a highly separable low dimensionality encoding employing t-distributed Stochastic Neighbourhood Embedding, and, 2) central to this communication, a multi-label classifier that boosts classification rates by exploiting the fact that the classes corresponding to the individual exercises are naturally organized as a network. Depending on the targeted therapeutic movement class labels i.e. exercises scores, are highly correlated-patients who perform well in one, tends to perform well in related exercises-; and critically no node can be used as proxy of others - an exercise does not encode the information of other exercises. Over data from a cohort of 20 patients, the novel classifier outperforms classical Naive Bayes, random forest and variants of support vector machines (ANOVA: p <; 0.001). The novel multi-label classification strategy fulfills an automatic system for motor dexterity assessment, with implications for lessening therapist's workloads, reducing healthcare costs and providing support for home-based virtual rehabilitation and telerehabilitation alternatives.
Editor(s)
Amirabdollahian, F
Burdet, E
Masia, L
Date Issued
2017-08-15
Date Acceptance
2017-07-17
Citation
2017 INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR), 2017, pp.521-526
ISSN
1945-7898
Publisher
IEEE
Start Page
521
End Page
526
Journal / Book Title
2017 INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS (ICORR)
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000426850800090&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
International Conference on Rehabilitation Robotics (ICORR)
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Electrical & Electronic
Robotics
Rehabilitation
Engineering
MULTI-LABEL CLASSIFICATION
REHABILITATION
RECOVERY
THERAPY
Publication Status
Published
Start Date
2017-07-17
Finish Date
2017-07-20
Coverage Spatial
London, ENGLAND