A novel system for automatic classification of upper limb motor function after stroke: an exploratory study.
Identifieur interne : 000210 ( PubMed/Checkpoint ); précédent : 000209; suivant : 000211A novel system for automatic classification of upper limb motor function after stroke: an exploratory study.
Auteurs : Vítor Tedim Cruz [Portugal] ; Virgílio Ferro Bento [Portugal] ; David Dieteren Ribeiro [Portugal] ; Isabel Araújo [Portugal] ; Catarina Aguiar Branco [Portugal] ; Paula Coutinho [Portugal]Source :
- Medical engineering & physics [ 1873-4030 ] ; 2014.
Descripteurs français
- KwdFr :
- Accident vasculaire cérébral (diagnostic), Accident vasculaire cérébral (physiopathologie), Activité motrice, Adulte, Adulte d'âge moyen, Algorithmes, Conception d'appareillage, Facteurs temps, Humains, Indice de gravité médicale, Membre supérieur (physiopathologie), Mâle, Phénomènes biomécaniques, Reconnaissance automatique des formes (), Soins ambulatoires (), Sujet âgé, Technologie sans fil (instrumentation).
- MESH :
- diagnostic : Accident vasculaire cérébral.
- physiopathologie : Accident vasculaire cérébral, Membre supérieur.
- instrumentation : Activité motrice, Adulte, Adulte d'âge moyen, Algorithmes, Conception d'appareillage, Facteurs temps, Humains, Indice de gravité médicale, Mâle, Phénomènes biomécaniques, Reconnaissance automatique des formes, Soins ambulatoires, Sujet âgé, Technologie sans fil.
English descriptors
- KwdEn :
- Adult, Aged, Algorithms, Ambulatory Care (methods), Biomechanical Phenomena, Equipment Design, Humans, Male, Middle Aged, Motor Activity, Pattern Recognition, Automated (methods), Severity of Illness Index, Stroke (diagnosis), Stroke (physiopathology), Time Factors, Upper Extremity (physiopathology), Wireless Technology (instrumentation).
- MESH :
- diagnosis : Stroke.
- instrumentation : Wireless Technology.
- methods : Ambulatory Care, Pattern Recognition, Automated.
- physiopathology : Stroke, Upper Extremity.
- Adult, Aged, Algorithms, Biomechanical Phenomena, Equipment Design, Humans, Male, Middle Aged, Motor Activity, Severity of Illness Index, Time Factors.
Abstract
In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.
DOI: 10.1016/j.medengphy.2014.09.009
PubMed: 25280582
Affiliations:
Links toward previous steps (curation, corpus...)
Links to Exploration step
pubmed:25280582Le document en format XML
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<front><div type="abstract" xml:lang="en">In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.</div>
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<Abstract><AbstractText>In the early post-stroke phase, when clinicians attempt to evaluate interventions and accurately measure motor performance, reliable tools are needed. Therefore, the development of a system capable of independent, repeated and automatic assessment of motor function is of increased importance. This manuscript explores the potential of a newly designed device for automatic assessment of motor impairment after stroke. A portable motion capture system was developed to acquire three-dimensional kinematics data of upper limb movements. These were then computed through an automatic decision tree classifier, with features inferred from the Functional Ability Score (FAS) of the Wolf Motor Function Test (WMFT). Five stroke patients were tested on both sides across five selected tasks. The system was compared against a trained clinician, operating simultaneously and blinded. Regarding performance time, the mean difference (system vs clinician) was 0.17s (sd=0.14s). For FAS evaluation, there was agreement in 4 out of 5 patients in the two tasks evaluated. The prototype tested was able to automatically classify upper limb movement, according to a widely used functional motor scale (WMFT) in a relevant clinical setting. These results represent an important step towards a system capable of precise and independent motor evaluation after stroke. The portability and low-cost design will contribute for its usability in ambulatory clinical settings and research trials.</AbstractText>
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<name sortKey="Araujo, Isabel" sort="Araujo, Isabel" uniqKey="Araujo I" first="Isabel" last="Araújo">Isabel Araújo</name>
<name sortKey="Bento, Virgilio Ferro" sort="Bento, Virgilio Ferro" uniqKey="Bento V" first="Virgílio Ferro" last="Bento">Virgílio Ferro Bento</name>
<name sortKey="Branco, Catarina Aguiar" sort="Branco, Catarina Aguiar" uniqKey="Branco C" first="Catarina Aguiar" last="Branco">Catarina Aguiar Branco</name>
<name sortKey="Coutinho, Paula" sort="Coutinho, Paula" uniqKey="Coutinho P" first="Paula" last="Coutinho">Paula Coutinho</name>
<name sortKey="Ribeiro, David Dieteren" sort="Ribeiro, David Dieteren" uniqKey="Ribeiro D" first="David Dieteren" last="Ribeiro">David Dieteren Ribeiro</name>
</country>
</tree>
</affiliations>
</record>
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