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![Fig. 2 Nomenclature of the bones in the human hand. From [12].](profile/Patrick-Van-Der-Smagt/publication/224999643/figure/fig2/AS:340622616612892@1458222168729/Nomenclature-of-the-bones-in-the-human-hand-From-12_Q320.jpg)
Even though there are many existing computational models of the kine-matics of the human hand, none of them has the required precision sufficient to allow for rebuilding a model of the human hand. Embedded in a larger project in building a robotic arm mimicking the dynamics and kinematics of the human hand and arm, our goal is to obtain a detailed...
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... Commercial companies have used MRI to build complete human body 3D anatomy [Zygote 2016]. A few publications analyzed the hand bone geometry using MRI scans [Miyata et al. 2005;Rusu 2011;Stillfried 2015;van der Smagt and Stillfried 2008]. However, because the MRI scanning times need to be long to decrease the signal to noise ratio (10-15 minutes in our work), prior work has not addressed an important limitation. ...
... Compared to the hip, a human hand (without the wrist) has 27 bones and 19 flexible joints, which dramatically increases the segmentation complexity. The most common approach (aimed at general human bodies, but also applicable to hands) in practice is to mostly perform a manual segmentation, with the help of a few basic computer vision techniques such as thresholding, filtering and water-shedding [Stillfried 2015;van der Smagt and Stillfried 2008]. Such capabilities are often integrated into commercial medical image analysis software, such as Amira [Amira 2018], VTK [VTK 2018] and ITK-SNAP [ITK-SNAP 2018]. ...
We demonstrate how to acquire complete human hand bone anatomy (meshes) in multiple poses using magnetic resonance imaging (MRI). Such acquisition was previously difficult because MRI scans must be long for high-precision results (over 10 minutes) and because humans cannot hold the hand perfectly still in non-trivial and badly supported poses. We invent a manufacturing process whereby we use lifecasting materials commonly employed in film special effects industry to generate hand molds, personalized to the subject, and to each pose. These molds are both ergonomic and encasing, and they stabilize the hand during scanning. We also demonstrate how to efficiently segment the MRI scans into individual bone meshes in all poses, and how to correspond each bone's mesh to same mesh connectivity across all poses. Next, we interpolate and extrapolate the MRI-acquired bone meshes to the entire range of motion of the hand, producing an accurate data-driven animation-ready rig for bone meshes. We also demonstrate how to acquire not just bone geometry (using MRI) in each pose, but also a matching highly accurate surface geometry (using optical scanners) in each pose, modeling skin pores and wrinkles. We also give a soft tissue Finite Element Method simulation "rig", consisting of novel tet meshing for stability at the joints, spatially varying geometric and material detail, and quality constraints to the acquired skeleton kinematic rig. Given an animation sequence of hand joint angles, our FEM soft tissue rig produces quality hand surface shapes in arbitrary poses in the hand range of motion. Our results qualitatively reproduce important features seen in the photographs of the subject's hand, such as similar overall organic shape and fold formation.
... I.4.1.2.1 Les systèmes de radiographie et de fluoroscopie, et les CT-scans Certains de ces auteurs ont effectué des radiographies de la main dans plusieurs positions différentes afin d'analyser les mouvements entre les os. Cependant, le gros inconvénient de la radiographie est que les rayons X sont extrêmement nocifs pour la santé (Smagt & Stillfried, 2008). En général, ces études sont généralement limitées à des cas ex vivo (Cooney et al., 1981;Imaeda et al., 1993) ou à des cas médicaux avec des patients (Kapandji & Kapandji, 1993). ...
... Si effectivement les RMSE des plans des moindres carrés obtenus pour ces articulations sont de l'ordre du centième de millimètre, les RMSE des cercles des moindres carrés sont supérieurs et de l'ordre de quelques dixièmes de millimètre. Une explication à ces résultats peut être le fait que les AoR des articulations IP1 du pouce, ainsi que des articulations IPP et IPD des doigts II à V, ne sont pas tout à fait fixes et qu'il existe des déplacements d'environ 1 mm des AoR (Smagt & Stillfried, 2008). Cette différence de RMSE entre les plans des moindres carrés et les cercles des moindres carrés peut être également due aux mouvements de peau qui ont lieu principalement suivant l'axe longitudinal . ...
... Si les faibles valeurs des RMSE obtenues en F-E pour les articulations MCP, TMC du pouce et RC du poignet, permettent d'assimiler la F-E à un mouvement plan et circulaire, et de modéliser ces articulations par des liaisons pivot en F-E, cette conclusion est en revanche moins évidente dans le cas du mouvement d'A-A du fait des valeurs importantes des RMSE des cercles des moindres carrés. En effet, van der Smagt et al (2008) considèrent que pour des déplacements d'environ 1 mm de la position des AoR, ces derniers peuvent être considérés comme fixes. En revanche, aucune précision n'est donnée dans le cas où les variations de la position de l'AoR atteignent 4,8 mm, comme c'est le cas dans l'articulation TMC (Smagt & Stillfried, 2008). ...
La main est l’organe de préhension chez l’homme qui lui permet de manipuler des objets de tailles et de formes diverses et variées. Elle lui permet également d’effectuer des tâches, parfois d’une grande complexité, avec force ou avec une grande finesse. Il s’agit d’un outil d’une importance cruciale au quotidien, tant sur le plan domestique que sur le plan professionnel, et la perte de fonctionnalité de la main peut rapidement devenir un handicap pour certaines personnes. Bien qu’il existe encore peu d’études sur la capture et l’analyse des mouvements de la main dans la littérature scientifique, de nombreux domaines comme la médecine, l’ergonomie, le sport, la robotique, la réalité virtuelle ou les jeux vidéo s’y intéressent de plus en plus, afin de permettre aux personnes d’exploiter au mieux les fonctionnalités de la main tout en la préservant, ou de créer des interfaces homme-machine afin que celui-ci puisse interagir avec des robots ou en environnement virtuel. L’objectif de cette thèse a été de développer une méthode d’analyse cinématique in vivo et personnalisée afin de contribuer à une meilleure compréhension des mouvements de la main de l’homme. Une première partie du travail consiste à mettre en place un protocole de capture du mouvement de la main sur des sujets masculins et féminins de différentes tranches d’âges, allant de 20 à 50 ans. Les captures de mouvement ont été effectuées à l’aide d’un système optoélectronique et de 56 marqueurs passifs collés sur la peau de la main du sujet. Deux types de mouvements ont été capturés : les mouvements fonctionnels en flexion-extension et en abduction-adduction de la main, et les mouvements de prise d’objets cylindriques et sphériques. Les captures de mouvement sont ensuite labellisées afin d’identifier les différents marqueurs et de pouvoir en extraire les trajectoires. La deuxième partie du travail consiste à développer une méthode d’analyse cinématique des mouvements externes de la main à partir des trajectoires des marqueurs et de la valider à l’aide d’un modèle in silico. En plus d’estimer les paramètres cinématiques avec précision dans le cas où les trajectoires des marqueurs sont parfaites, l’évaluation des méthodes fonctionnelles montre que les mouvements de la main sont assimilables à des mouvements plans, circulaires ou sphériques en fonction de l’articulation étudiée. La construction des repères fonctionnels à partir des paramètres cinématiques pour chaque segment de la main permet ensuite de décrire les mouvements de rotation des articulations à l’aide des angles de Cardan. Les courbes des angles de Cardan obtenues à partir des trajectoires des marqueurs du modèle in silico permettent de valider la méthode de décomposition développée pour l’ensemble des articulations, à l’exception des articulations trapézo-métacarpienne (TMC) et métacarpo-phalangienne (MCP1) du pouce qui sont plus difficiles à analyser. La dernière partie de ce travail de thèse consiste à analyser les mouvements fonctionnels et les mouvements de prise d’objets à partir des captures expérimentales. Les courbes des angles de Cardan obtenues à partir des mouvements fonctionnels correspondent globalement à celles obtenues dans la littérature, sauf dans le cas de l’articulation TMC. De plus, ces courbes montrent que les rotations articulaires ne s’effectuent pas uniquement autour d’un axe, mais autour d’un axe de rotation dominant et d’un ou deux axes de rotation secondaires. Toutefois, les courbes des angles de Cardan des rotations autour des axes secondaires ne correspondent pas toujours à celles présentées dans la littérature. Bien que peu de mouvements de prise d’objets aient pu être analysés, certaines corrélations intéressantes ont pu être trouvées entre les postures de la main et la géométrie des objets manipulés, notamment au niveau des articulations MCP et IPD.
This paper considers the design of an anthropomorphic manipulator in the form of a human hand. The aim of the work is to describe the kinematics of a robotic hand, using the example of one finger. Based on the Denavit–Hartenberg algorithm, a kinematic model is developed for one finger of the robotic hand. The working area of the robotic finger is determined, taking into account the limitations on the rotation angles of the finger phalanges. These kinematic equations are used to solve the problem of the speeds of one fingertip. According to the results of modeling for given speeds of the fingertip, the dependencies of the rotation angles of the robotic hand links were obtained. Unlike the previously used models, the developed one makes it possible to take into account the presence of a geometric constraint between the proximal and distal finger phalanges, which is structurally made with an inextensible thread. The results of the work can be used in the design and manufacture of new robotic gloves.
Precision modeling of the hand internal musculoskeletal anatomy has been largely limited to individual poses, and has not been connected into continuous volumetric motion of the hand anatomy actuating across the hand's entire range of motion. This is for a good reason, as hand anatomy and its motion are extremely complex and cannot be predicted merely from the anatomy in a single pose. We give a method to simulate the volumetric shape of hand's musculoskeletal organs to any pose in the hand's range of motion, producing external hand shapes and internal organ shapes that match ground truth optical scans and medical images (MRI) in multiple scanned poses. We achieve this by combining MRI images in multiple hand poses with FEM multibody nonlinear elastoplastic simulation. Our system models bones, muscles, tendons, joint ligaments and fat as separate volumetric organs that mechanically interact through contact and attachments, and whose shape matches medical images (MRI) in the MRI-scanned hand poses. The match to MRI is achieved by incorporating pose-space deformation and plastic strains into the simulation. We show how to do this in a non-intrusive manner that still retains all the simulation benefits, namely the ability to prescribe realistic material properties, generalize to arbitrary poses, preserve volume and obey contacts and attachments. We use our method to produce volumetric renders of the internal anatomy of the human hand in motion, and to compute and render highly realistic hand surface shapes. We evaluate our method by comparing it to optical scans, and demonstrate that we qualitatively and quantitatively substantially decrease the error compared to previous work. We test our method on five complex hand sequences, generated either using keyframe animation or performance animation using modern hand tracking techniques.
The actual movement and the coupled relationship of human fingers are very important for the analysis of human hand grasp, but there are very few effective ways to detect it. The paper introduces a method with wearable measuring sensors to realize the real time measurement and description of human fingers’ movement during hand grasping. A data glove for real-time measuring the movement of each joint of all the fingers was designed. The finger movements of three typical human hand grasps were measured, and the coupled relationship and the complete motion trajectory of all five fingers were got and described.
The primate hand has long intrigued researchers of different disciplines. The extensive and elegant work of Napier included careful observations about the anatomy and function of the primate hand. While such observations and inferences substantially advanced our understanding of the primate hand, a more complete insight into the function of a complex organ such as the hand requires experimental investigation to illuminate patterns of joint movement, muscle activity, and loads. In the last decades, researchers have collected a wealth of information about hand morphology and function by setting up and conducting laboratory-based experiments. In this chapter, we give a comprehensive overview of the experimental work on the nonhuman primate hand that has been done since Napier’s publications in the 1950s. We discuss the different methods that are being used to study hand function: behavioral studies, kinematics, kinetics, dynamic palmar pressure, electromyography, medical imaging and computer modeling. Ultimately, studies focusing on hand form and function, especially those that include a diversity of extant species and integrate different types of data, will lead to a better understanding of the evolution of the human hand.
An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynamics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.
