Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study
Identifieur interne : 001348 ( Main/Curation ); précédent : 001347; suivant : 001349Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study
Auteurs : Ian Williams [Royaume-Uni] ; Timothy G. Constandinou [Royaume-Uni]Source :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2014.
Abstract
Accurate models of proprioceptive neural patterns could 1 day play an important role in the creation of an intuitive proprioceptive neural prosthesis for amputees. This paper looks at combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns for future experimental work in prosthesis feedback. A neuro-musculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. Unlike previous neuro-musculoskeletal models, muscle activation and excitation levels are unknowns in this application and an inverse dynamics tool (static optimization) is integrated to estimate these variables. A proprioceptive prosthesis will need to be portable and this is incompatible with the computationally demanding nature of standard biomechanical and proprioceptor modeling. This paper uses and proposes a number of approximations and optimizations to make real time operation on portable hardware feasible. Finally technical obstacles to mimicking natural feedback for an intuitive proprioceptive prosthesis, as well as issues and limitations with existing models, are identified and discussed.
Url:
DOI: 10.3389/fnins.2014.00181
PubMed: 25009463
PubMed Central: 4069835
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Constandinou</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Department of Electrical and Electronic Engineering, Imperial College London</institution><country>London, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Center for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London</institution><country>London, UK</country></nlm:aff><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea></wicri:regionArea><wicri:regionArea># see nlm:aff region in country</wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Neuroscience</title><idno type="ISSN">1662-4548</idno><idno type="eISSN">1662-453X</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>Accurate models of proprioceptive neural patterns could 1 day play an important role in the creation of an intuitive proprioceptive neural prosthesis for amputees. This paper looks at combining efficient implementations of biomechanical and proprioceptor models in order to generate signals that mimic human muscular proprioceptive patterns for future experimental work in prosthesis feedback. A neuro-musculoskeletal model of the upper limb with 7 degrees of freedom and 17 muscles is presented and generates real time estimates of muscle spindle and Golgi Tendon Organ neural firing patterns. Unlike previous neuro-musculoskeletal models, muscle activation and excitation levels are unknowns in this application and an inverse dynamics tool (static optimization) is integrated to estimate these variables. A proprioceptive prosthesis will need to be portable and this is incompatible with the computationally demanding nature of standard biomechanical and proprioceptor modeling. This paper uses and proposes a number of approximations and optimizations to make real time operation on portable hardware feasible. Finally technical obstacles to mimicking natural feedback for an intuitive proprioceptive prosthesis, as well as issues and limitations with existing models, are identified and discussed.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Al Falahe, N" uniqKey="Al Falahe N">N. al Falahe</name></author><author><name sortKey="Nagaoka, M" uniqKey="Nagaoka M">M. Nagaoka</name></author><author><name sortKey="Vallbo, A" uniqKey="Vallbo A">A. Vallbo</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Biran, R" uniqKey="Biran R">R. Biran</name></author><author><name sortKey="Martin, D C" uniqKey="Martin D">D. C. Martin</name></author><author><name sortKey="Tresco, P A" uniqKey="Tresco P">P. A. Tresco</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Blank, A" uniqKey="Blank A">A. Blank</name></author><author><name sortKey="Okamura, A M" uniqKey="Okamura A">A. M. 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