Robotics-based Synthesis of Human Motion
Identifieur interne : 002053 ( Pmc/Checkpoint ); précédent : 002052; suivant : 002054Robotics-based Synthesis of Human Motion
Auteurs : O. Khatib [États-Unis] ; E. Demircan [États-Unis] ; V. De Sapio [États-Unis] ; L. Sentis [États-Unis] ; T. Besier [États-Unis] ; S. Delp [États-Unis]Source :
- Journal of physiology, Paris [ 0928-4257 ] ; 2009.
Abstract
The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics may be leveraged to provide novel musculoskeletal modeling methods and physiologically accurate performance predictions. In this paper, we present (i) a new method for the real-time reconstruction of human motion trajectories using direct marker tracking, (ii) a task-driven muscular effort minimization criterion and (iii) new human performance metrics for dynamic characterization of athletic skills. Dynamic motion reconstruction is achieved through the control of a simulated human model to follow the captured marker trajectories in real-time. The operational space control and real-time simulation provide human dynamics at any configuration of the performance. A new criteria of muscular effort minimization has been introduced to analyze human static postures. Extensive motion capture experiments were conducted to validate the new minimization criterion. Finally, new human performance metrics were introduced to study in details an athletic skill. These metrics include the effort expenditure and the feasible set of operational space accelerations during the performance of the skill. The dynamic characterization takes into account skeletal kinematics as well as muscle routing kinematics and force generating capacities. The developments draw upon an advanced musculoskeletal modeling platform and a task-oriented framework for the effective integration of biomechanics and robotics methods.
Url:
DOI: 10.1016/j.jphysparis.2009.08.004
PubMed: 19665552
PubMed Central: 2782476
Affiliations:
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<front><div type="abstract" xml:lang="en"><p id="P1">The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics may be leveraged to provide novel musculoskeletal modeling methods and physiologically accurate performance predictions. In this paper, we present (i) a new method for the real-time reconstruction of human motion trajectories using direct marker tracking, (ii) a task-driven muscular effort minimization criterion and (iii) new human performance metrics for dynamic characterization of athletic skills. Dynamic motion reconstruction is achieved through the control of a simulated human model to follow the captured marker trajectories in real-time. The operational space control and real-time simulation provide human dynamics at any configuration of the performance. A new criteria of muscular effort minimization has been introduced to analyze human static postures. Extensive motion capture experiments were conducted to validate the new minimization criterion. Finally, new human performance metrics were introduced to study in details an athletic skill. These metrics include the effort expenditure and the feasible set of operational space accelerations during the performance of the skill. The dynamic characterization takes into account skeletal kinematics as well as muscle routing kinematics and force generating capacities. The developments draw upon an advanced musculoskeletal modeling platform and a task-oriented framework for the effective integration of biomechanics and robotics methods.</p>
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<title-group><article-title>Robotics-based Synthesis of Human Motion</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Khatib</surname>
<given-names>O.</given-names>
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<xref rid="A1" ref-type="aff">a</xref>
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<contrib contrib-type="author"><name><surname>De Sapio</surname>
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<contrib contrib-type="author"><name><surname>Sentis</surname>
<given-names>L.</given-names>
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<email>lsentis@cs.stanford.edu</email>
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<contrib contrib-type="author"><name><surname>Besier</surname>
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<aff id="A1"><label>a</label>
Artificial Intelligence Laboratory, Stanford University, Stanford, CA 94305, USA</aff>
<aff id="A2"><label>b</label>
Sandia National Laboratories, Livermore, CA 94551, USA</aff>
<aff id="A3"><label>c</label>
Human Performance Laboratory, Stanford, CA 94305, USA</aff>
<aff id="A4"><label>d</label>
Neuromuscular Biomechanics Laboratory, Stanford, CA 94305, USA</aff>
<pub-date pub-type="nihms-submitted"><day>23</day>
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<year>2009</year>
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<year>2009</year>
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<pub-date pub-type="pmc-release"><day>1</day>
<month>9</month>
<year>2010</year>
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<issue>3-5</issue>
<fpage>211</fpage>
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<permissions><copyright-statement>© 2009 Elsevier Ltd. All rights reserved.</copyright-statement>
<copyright-year>2009</copyright-year>
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<abstract><p id="P1">The synthesis of human motion is a complex procedure that involves accurate reconstruction of movement sequences, modeling of musculoskeletal kinematics, dynamics and actuation, and characterization of reliable performance criteria. Many of these processes have much in common with the problems found in robotics research. Task-based methods used in robotics may be leveraged to provide novel musculoskeletal modeling methods and physiologically accurate performance predictions. In this paper, we present (i) a new method for the real-time reconstruction of human motion trajectories using direct marker tracking, (ii) a task-driven muscular effort minimization criterion and (iii) new human performance metrics for dynamic characterization of athletic skills. Dynamic motion reconstruction is achieved through the control of a simulated human model to follow the captured marker trajectories in real-time. The operational space control and real-time simulation provide human dynamics at any configuration of the performance. A new criteria of muscular effort minimization has been introduced to analyze human static postures. Extensive motion capture experiments were conducted to validate the new minimization criterion. Finally, new human performance metrics were introduced to study in details an athletic skill. These metrics include the effort expenditure and the feasible set of operational space accelerations during the performance of the skill. The dynamic characterization takes into account skeletal kinematics as well as muscle routing kinematics and force generating capacities. The developments draw upon an advanced musculoskeletal modeling platform and a task-oriented framework for the effective integration of biomechanics and robotics methods.</p>
</abstract>
<kwd-group><title>Index Terms</title>
<kwd>task-space framework</kwd>
<kwd>human motion analysis</kwd>
<kwd>robotics</kwd>
<kwd>musculoskeletal dynamics</kwd>
<kwd>human animation</kwd>
<kwd>operational space formulation</kwd>
</kwd-group>
<contract-num rid="GM1">U54 GM072970-01
||GM</contract-num>
<contract-sponsor id="GM1">National Institute of General Medical Sciences : NIGMS</contract-sponsor>
</article-meta>
</front>
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<affiliations><list><country><li>États-Unis</li>
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<region><li>Californie</li>
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<tree><country name="États-Unis"><region name="Californie"><name sortKey="Khatib, O" sort="Khatib, O" uniqKey="Khatib O" first="O." last="Khatib">O. Khatib</name>
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<name sortKey="Besier, T" sort="Besier, T" uniqKey="Besier T" first="T." last="Besier">T. Besier</name>
<name sortKey="De Sapio, V" sort="De Sapio, V" uniqKey="De Sapio V" first="V." last="De Sapio">V. De Sapio</name>
<name sortKey="De Sapio, V" sort="De Sapio, V" uniqKey="De Sapio V" first="V." last="De Sapio">V. De Sapio</name>
<name sortKey="Delp, S" sort="Delp, S" uniqKey="Delp S" first="S." last="Delp">S. Delp</name>
<name sortKey="Demircan, E" sort="Demircan, E" uniqKey="Demircan E" first="E." last="Demircan">E. Demircan</name>
<name sortKey="Sentis, L" sort="Sentis, L" uniqKey="Sentis L" first="L." last="Sentis">L. Sentis</name>
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