Imperial College London
Dr Felipe Orihuela-Espina

DrFelipeOrihuela-Espina

Faculty of Medicine, Department of Surgery & Cancer

Honorary Lecturer
 
 
 
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Publications

Citation

BibTex format

@inproceedings{Heyer:2017:10.1109/ICORR.2017.8009301,
author = {Heyer, P and Castrejon, LR and Orihuela-Espina, F and Sucar, LE},
doi = {10.1109/ICORR.2017.8009301},
pages = {521--526},
publisher = {IEEE},
title = {Automation of motor dexterity assessment},
url = {http://dx.doi.org/10.1109/ICORR.2017.8009301},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - 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.
AU  - Heyer,P
AU  - Castrejon,LR
AU  - Orihuela-Espina,F
AU  - Sucar,LE
DO  - 10.1109/ICORR.2017.8009301
EP  - 526
PB  - IEEE
PY  - 2017///
SN  - 1945-7898
SP  - 521
TI  - Automation of motor dexterity assessment
UR  - http://dx.doi.org/10.1109/ICORR.2017.8009301
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000426850800090&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/58630
ER  -
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