Computational Motor Control of Human Movements

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Computational Motor Control of Human Movements Technische Universit¨atDarmstadt (D 17) Fachbereich Humanwissenschaften Computational Motor Control of Human Movements Dissertation zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) vorgelegt von Diplom-Sportwiss. Thorsten Stein geboren in Langen Eingereicht am 01. Dezember 2009 Disputation am 11. Februar 2010 Erstgutachter: Prof. Dr. Josef Wiemeyer Zweitgutachter: Prof. Dr. Hermann Schwameder (Karlsruher Institut f¨urTechnologie) Darmstadt 2010 This thesis was composed with KOMA-Script and LATEX. For my parents, Anita M. Stein and Walter Stein Acknowledgments First of all, I would like to express my gratitude to my parents and my grandma for reasons too numerous to mention here! Moreover, I am profoundly grateful to Ilka for her love, active support and for her patience with someone who was glued to his desk and lost in his own thoughts during the last weeks of writing. Finally, I would like to thank “the boys” for keeping their fingers crossed for me. During my studies, Prof. Dr. J. Wiemeyer aroused my interest in the topic “human motor control”. For this and for reviewing my thesis, I am deeply thankful. Moreover, I am profoundly grateful to Prof. Dr. H. Schwameder, who was always encouraging and supportive of my work. Our discussions were very helpful in shaping my ideas. At the same time, he gave me the freedom and the resources to pursue my own research goals. I also like to thank Prof. Dr. K. B¨os for believing in my abilities. Thanks to my colleagues at the Department of Sport and Sport Science - espe- cially to Dr. M. Wagner, A. Schnur, B. Hermann and A. Fischer - for the close collaboration. In particular, I would like to thank Andreas for the great cooperation during the last five years, the many interesting discussions in our shared offices and for introducing me to biomechanical data acquisition. Additionally, I sincerely appreciated the interdisciplinary cooperation with the colleagues from the Collaborative Research Center 588 “Humanoid Robots” during the last five years, especially D. Gehrig, I. Boesnach, Dr. J. Moldenhauer, H. K¨uhne, Dr. A. W¨ornerand Prof. Dr. T. Schultz from the Department of Computer Sciences as well as Dr. C. Simonidis, Dr. G. Stelzner, F. Bauer and Prof. Dr. W. Seemann from the Department of Mechanical Engineering. In particular, I would like to thank Christian for the great cooperation and many hours of stimulating discussions on the topics biomechanical modeling and optimal control in the context of human motor control. This work was supported by the German Research Foundation within Collabo- rative Research Center (CRC) 588 “Humanoid Robots - Learning and Cooperating Multimodal Robots”. v Contents Acknowledgments vi Zusammenfassung ix Summary xv 1 Introduction 1 1.1 Motivation . 1 1.2 Structure of the thesis . 5 2 Human motor control 9 2.1 Introduction . 9 2.2 The degrees of freedom problem . 12 2.3 Scientific approaches in the study of human motor control . 19 2.3.1 Motor approach . 20 2.3.2 Dynamical systems approach . 31 2.3.3 Computational neuroscience approach . 40 2.4 A computational approach for studying human movements . 83 2.4.1 Fundamental considerations about the study of human motor control . 84 2.4.2 Implementation of the considerations in the context of the de- grees of freedom problem . 91 3 Methods 95 3.1 Subjects . 95 3.2 Procedures . 95 3.3 Motion capturing . 97 3.3.1 Data acquisition . 97 3.3.2 Data processing . 98 3.4 Biomechanical modeling . 99 3.4.1 Dynamic equations . 100 3.4.2 Motion mapping and inverse calculations . 101 3.5 Data analysis . 104 vii Contents 4 Study I: The analysis of multi-joint pointing movements in 3D space 105 4.1 Introduction . 105 4.2 Methods . 110 4.3 Results . 110 4.3.1 Hand kinematics . 111 4.3.2 Joint kinematics . 112 4.3.3 Joint torques . 123 4.4 Discussion . 123 4.4.1 Biological motor control . 123 4.4.2 Robotics . 126 4.4.3 Applied methods . 127 5 Study II: The synthesis of multi-joint pointing movements in 3D space 129 5.1 Introduction . 129 5.2 Methods . 134 5.2.1 Subjects . 134 5.2.2 Procedures . 134 5.2.3 Data acquisition and processing . 134 5.2.4 Biomechanical modeling . 134 5.2.5 Optimal control models . 134 5.2.6 Optimization method . 135 5.2.7 Simulation protocol . 137 5.2.8 Data analysis . 139 5.3 Results . 144 5.3.1 Validation of the optimization method . 144 5.3.2 Target 1: Comparison of the measured and predicted trajectories147 5.3.3 Target 3: Comparison of the measured and predicted trajectories181 5.4 Discussion . 220 5.4.1 Biological motor control . 220 5.4.2 Robotics . 231 5.4.3 Applied methods . 233 6 Summary 241 6.1 Results . 243 6.2 Further research . 246 References 249 Eidesstattliche Erkl¨arung 285 Curriculum Vitae 287 viii Zusammenfassung Die vorliegende Arbeit entstand im Rahmen des DFG-Sonderforschungsbereiches 588 “Humanoide Roboter - Lernende und kooperierende multimodale Roboter”. Eines der herausragenden Ziele der internationalen Robotik-Community ist die Kon- struktion von Robotern, die in den Alltag des Menschen integriert werden k¨onnen. Dabei wird der Standpunkt vertreten, dass diese Maschinen humanoid sein sollen, um die Interaktion zwischen Mensch und Maschine zu erleichtern. Humanoid be- deutet, dass Gr¨oße und Anatomie sowie die Anzahl der Bewegungsfreiheitsgrade und die Bewegungsamplituden in den einzelnen Gelenken dem menschlichen Vorbild nachempfunden sein sollen. Gleich dem biologischen Original verf¨ugteine solche Ma- schine ¨uber redundante Bewegungsfreiheitsgrade. Das redundante Design erm¨oglicht dem Roboter Hindernissen auszuweichen und w¨ahrend der Bewegungsausf¨uhrung Gelenkanschl¨age zu umgehen. Allerdings wird diese Flexibilit¨atmit einem Kon- trollproblem erkauft. Welche Bewegungsl¨osungsoll der Roboter aus den vielen m¨oglichen L¨osungenin der jeweiligen Situation ausw¨ahlen? Auf der Grundlage der bisherigen Ausf¨uhrungen sollte die Maschine m¨oglichst menschen¨ahnliche Bewe- gungen ausf¨uhren.Demnach lautet die zu beantwortende Frage: Auf der Grundlage welcher Prinzipien wird im menschlichen ZNS f¨ureine bestimmte Bewegungsaufgabe eine Bewegungsl¨osung aus den vielen m¨oglichen Bewegungsl¨osungen ausgew¨ahlt? W¨aren diese Prinzipien bekannt, m¨ussten sie in die formale Sprache der Mathematik ¨ubersetzt werden und w¨aren somit zumindest prinzipiell einer computergesteuerten Maschine zug¨anglich. Bis heute haben Bewegungswissenschaftler keine zufrieden- stellenden Antworten auf die Frage, auf der Grundlage welcher Prinzipien im ZNS die Selektion einer Bewegungsl¨osung erfolgt. Dieses Forschungsdefizit ist der Aus- gangspunkt der vorliegenden Arbeit. Da eine Probleml¨osungeine detaillierte Problemkenntnis voraussetzt, beginnt der Theorieteil dieser Arbeit mit einer ausf¨uhrlichen Aufarbeitung des Problems der re- dundanten Bewegungsfreiheitsgrade. Darauf aufbauend werden in den folgenden Abschnitten die aktuell einflussreichsten L¨osungsans¨atze,jeweils eingebettet in den entsprechenden paradigmatischen Rahmen, dargestellt und diskutiert. Es sind dies Modelle, die dem Informationsverarbeitungsansatz, dem dynamischen Systemansatz sowie der komputationalen Neurowissenschaft zugeordnet werden k¨onnen. Dieses Review bildet die Grundlage f¨urden komputationalen Ansatz dieser Arbeit, der biomechanische Experimente mit mathematischen Modellierungen, Computersimu- lationen und Zeitreihenanalysen verbindet und eine Integration von neurophysio- logischen, biomechanischen und verhaltenswissenschaftlichen Befunden erm¨oglicht. ix Zusammenfassung Bei der Entwicklung von Modellen ist zu beachten, dass Modelle ihr jeweiliges Origi- nal nur in Teilen repr¨asentieren, d.h. sie erfassen nicht alle Attribute des Originalsys- tems. Dementsprechend k¨onnen Modelle der motorischen Kontrolle auch nicht alle Arten menschlicher Bewegungen erkl¨aren. Im Kontext des Problems der redundanten Bewegungsfreiheitsgrade stellt die Analyse mehrgelenkiger Bewegungen im 3D-Raum ein aktuelles Forschungsfeld der internationalen Bewegungswissenschaften dar. Aus diesem Grund wird in der vorliegenden Arbeit ein Modell entwickelt, das die Unter- suchung von Kontrollvorg¨angenim menschlichen ZNS bei der Ausf¨uhrung mehrge- lenkiger Bewegungen im 3D-Raum erm¨oglicht. Das Modell wird in der vorliegenden Arbeit zur Untersuchung mehrgelenkiger Zeigebewegungen im 3D-Raum in einem nat¨urlichen Kontext eingesetzt. Diese Bewegungen sind weniger komplex als die mei- sten sportlichen Bewegungen. Im Gegensatz zu stark vereinfachten Bewegungsauf- gaben unter Laborbedingungen sind diese Bewegungen aber trotzdem “¨okologisch valide”, da Menschen auf diese Bewegungen in ihrem Alltag h¨aufig zur¨uckgreifen. Schnelle, hochge¨ubteZeigebewegungen auf Ziele, die sich im unmittelbaren Sicht- feld der Person befinden, sind h¨ochstwahrscheinlich feedforward kontrolliert. Dabei wird in der vorliegenden Arbeit davon ausgegangen, dass im ZNS eine Planungs- instanz einen Bewegungsentwurf bzw. einen Bewegungsplan erstellt und diesen an eine Kontrollinstanz weiterleitet, die die entsprechenden Muskelkommandos berech- net. Diese Kommandos werden schließlich an das Muskelskelettsystem gesendet, das die Kommandos in eine Bewegung umsetzt. Da das motorische System ¨uber eine sehr große Anzahl an Bewegungsfreiheitsgraden verf¨ugt, existieren eine Vielzahl von m¨oglichen zielf¨uhrendenBewegungspl¨anen.Um besser zu verstehen, auf Grund welcher Prinzipien im ZNS ein Bewegungsplan ausgew¨ahlt wird und auf welcher Ebene diese Prinzipien arbeiten, wurden zwei Studien durchgef¨uhrt.Beiden Studien liegt ein Experiment zugrunde, bei dem 20
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