Velocity based controllers for dynamic character animation

Dynamic character animation is a technique used to create character movements based on physics laws. Proportional derivative (PD) controllers are one of the preferred techniques in real time character simulations for driving the state of the character from its current state to a new target-state. In this paper is presented an alternative approach named velocity based controllers that are able to introduce into the dynamical system desired limbs relative velocities as constraints. As a result, the presented technique takes into account all the dynamical system to calculate the forces that transform our character from its current state to the target-state. This technique allows realtime simulation, uses a straightforward parameterization for the character muscle force capabilities and it is robust to disturbances. The paper shows the controllers capabilities for the case of human gait animation.Postprint (published version

Applications of a Biomechanical Patient Model for Adaptive Radiation Therapy

Biomechanical patient modeling incorporates physical knowledge of the human anatomy into the image processing that is required for tracking anatomical deformations during adaptive radiation therapy, especially particle therapy. In contrast to standard image registration, this enforces bio-fidelic image transformation. In this thesis, the potential of a kinematic skeleton model and soft tissue motion propagation are investigated for crucial image analysis steps in adaptive radiation therapy. The first application is the integration of the kinematic model in a deformable image registration process (KinematicDIR). For monomodal CT scan pairs, the median target registration error based on skeleton landmarks, is smaller than (1.6 ± 0.2) mm. In addition, the successful transferability of this concept to otherwise challenging multimodal registration between CT and CBCT as well as CT and MRI scan pairs is shown to result in median target registration error in the order of 2 mm. This meets the accuracy requirement for adaptive radiation therapy and is especially interesting for MR-guided approaches. Another aspect, emerging in radiotherapy, is the utilization of deep-learning-based organ segmentation. As radiotherapy-specific labeled data is scarce, the training of such methods relies heavily on augmentation techniques. In this work, the generation of synthetically but realistically deformed scans used as Bionic Augmentation in the training phase improved the predicted segmentations by up to 15% in the Dice similarity coefficient, depending on the training strategy. Finally, it is shown that the biomechanical model can be built-up from automatic segmentations without deterioration of the KinematicDIR application. This is essential for use in a clinical workflow

Articulated Statistical Shape Modelling of the Shoulder Joint

The shoulder joint is the most mobile and unstable joint in the human body. This makes it vulnerable to soft tissue pathologies and dislocation. Insight into the kinematics of the joint may enable improved diagnosis and treatment of different shoulder pathologies. Shoulder joint kinematics can be influenced by the articular geometry of the joint. The aim of this project was to develop an analysis framework for shoulder joint kinematics via the use of articulated statistical shape models (ASSMs). Articulated statistical shape models extend conventional statistical shape models by combining the shape variability of anatomical objects collected from different subjects (statistical shape models), with the physical variation of pose between the same objects (articulation). The developed pipeline involved manual annotation of anatomical landmarks selected on 3D surface meshes of scapulae and humeri and establishing dense surface correspondence across these data through a registration process. The registration was performed using a Gaussian process morphable model fitting approach. In order to register two objects separately, while keeping their shape and kinematics relationship intact, one of the objects (scapula) was fixed leaving the other (humerus) to be mobile. All the pairs of registered humeri and scapulae were brought back to their native imaged position using the inverse of the associated registration transformation. The glenohumeral rotational center and local anatomic coordinate system of the humeri and scapulae were determined using the definitions suggested by the International Society of Biomechanics. Three motions (flexion, abduction, and internal rotation) were generated using Euler angle sequences. The ASSM of the model was built using principal component analysis and validated. The validation results show that the model adequately estimated the shape and pose encoded in the training data. Developing ASSM of the shoulder joint helps to define the statistical shape and pose parameters of the gleno humeral articulating surfaces. An ASSM of the shoulder joint has potential applications in the analysis and investigation of population-wide joint posture variation and kinematics. Such analyses may include determining and quantifying abnormal articulation of the joint based on the range of motion; understanding of detailed glenohumeral joint function and internal joint measurement; and diagnosis of shoulder pathologies. Future work will involve developing a protocol for encoding the shoulder ASSM with real, rather than handcrafted, pose variation

Wearable kinesthetic system for capturing and classifying upper limb gesture in post-stroke rehabilitation

BACKGROUND: Monitoring body kinematics has fundamental relevance in several biological and technical disciplines. In particular the possibility to exactly know the posture may furnish a main aid in rehabilitation topics. In the present work an innovative and unobtrusive garment able to detect the posture and the movement of the upper limb has been introduced, with particular care to its application in post stroke rehabilitation field by describing the integration of the prototype in a healthcare service. METHODS: This paper deals with the design, the development and implementation of a sensing garment, from the characterization of innovative comfortable and diffuse sensors we used to the methodologies employed to gather information on the posture and movement which derive from the entire garments. Several new algorithms devoted to the signal acquisition, the treatment and posture and gesture reconstruction are introduced and tested. RESULTS: Data obtained by means of the sensing garment are analyzed and compared with the ones recorded using a traditional movement tracking system. CONCLUSION: The main results treated in this work are summarized and remarked. The system was compared with a commercial movement tracking system (a set of electrogoniometers) and it performed the same accuracy in detecting upper limb postures and movements

Articulated patient model in high-precision radiation therapy

In modern high precision radiotherapy, changes in the anatomy of the patient over the course of treatment pose a major challenge. An accurate assessment of occurring anatomical variations is the key requirement to enable an adaptation of the treatment plan for ensuring a highly precise treatment. Comparison of commonly used deformable image registration shows large discrepancies regarding the quality of anatomical alignment, benchmarked on a common data pool. One of the main reasons is found in widely used transformation models, insufficiently reflecting the actual deformation behaviour of the underlying tissue. Thus, especially in the highly heterogeneous head and neck area, which is characterized by many organs at risk being in proximity to the tumor as well as posture changes induced by the interplay of several bones, an accurate assessment of anatomical changes is essential for a successful adaptive radiotherapy. A physically meaningful transformation model offering a high biofidelity is required to provide an accurate anatomical alignment in such area. In this work, a novel biomechanical deformation model based on kinematics and multi-body physics for the whole head and neck area is introduced to guarantee the representation of physically meaningful transformations. The developed kinematic model is individually tailored to each patient as it is based on the delineated bones extracted from the computer tomography scan. It encompasses all bones relevant for head and neck cancer treatment, including bones of the proximal upper extremities, the shoulder girdle, cranial region, the rib cage and the vertebral column. Moreover, the model is designed to be easily extendible to other body regions. All bones are connected by ball and socket joints, which are automatically localized based on their individual geometries. A kinematic graph maintains the hierarchy of the connected bones across the whole skeleton to enable the propagation of local transformations to other body regions by inverse kinematics. Accuracy, robustness and computational efficiency of the kinematic model were retrospectively evaluated on patient datasets representative for typical inter-fractional variations as well as separately acquired image scans with large arms-up to arms-down posture changes. Using landmarks defined by multiple observers as reference, the overall mean accuracy of the kinematic model in reproducing postures in the image scans was found to be around 1 millimetre, which is settled slightly above the inter-observer variation. In detail, the assessed accuracy revealed potential for improvement regarding the automated positioning of the intervertebral joints in the region of the cervical spine. Due to the complex shape of the vertebrae, a relocation of the joint rotation centres towards the line connecting the centres of the intervertebral disks seems beneficial. Moreover, the use of ball and socket joints for the acromioclavicular joints has shown to be insufficient for mimicking the large arms-up to arms-down posture change due to the lack of representing translational offsets, observed in the image scans. The strong regularization of the permissible deformations in the skeletal anatomy leads to a higher robustness against conflicting input such as flawed or mixed-up anatomical feature points. Furthermore, such a physical-object-oriented transformation model requires even less input to describe meaningful deformations. With the total degrees of freedom of the kinematic head and neck model limited to those specified by the joints, the computation of new arbitrary skeletal postures is achieved within less than 50 milliseconds. With such efficient computation on the one hand and the strong regularization of deformations on the other hand, the kinematic model seems suitable for its application in a registration approach. In addition, it was demonstrated how the kinematic model can be successfully embedded into a registration approach as a transformation model to enable the fully automatic extraction of anatomical variations from image scans. This was accomplished by coupling the model to an extended simplex downhill optimizer and an overlap based similarity metric. The anatomy of pre-selected bones is aligned following a hierarchical optimization scheme. In conclusion, the novel developed kinematic model guarantees a deformation modelling of high biofidelity and efficiency, thus promising an assessment of anatomical changes without the need of an extensive visual inspection of the results as otherwise expected. To date, successful application of adaptive radiotherapy especially for tumors in regions characterized by a high anatomical flexibility is hampered by a lacking reliability of conventional deformation models. While associated uncertainties can be compensated at the cost of extended safety margins for photon therapy, prevailing range uncertainties when using particles currently impede the treatment of tumors in such areas. The dissemination of the proposed kinematic deformation model into the clinics provides a way to lay the foundation towards broadening the spectrum of patients eligible for treatment with particles, carried out at the increasing number of particle therapy centres worldwide

Multi-Contact Postures Computation on Manifolds

International audienceWe propose a framework to generate static robot configurations satisfying a set of physical and geometrical constraints. This is done by formulating nonlinear constrained optimization problems over non-Euclidean manifolds and solving them. To do so, we present a new sequential quadratic programming (SQP) solver working natively on general manifolds, and propose an interface to easily formulate the problems, with the tedious and error-prone work automated for the user. We also introduce several new types of constraints for having more complex contacts or working on forces/torques. Our approach allows an elegant mathematical description of the constraints and we exemplify it through formulation and computation examples in complex scenarios with humanoid robots

Simulating Humans: Computer Graphics, Animation, and Control

People are all around us. They inhabit our home, workplace, entertainment, and environment. Their presence and actions are noted or ignored, enjoyed or disdained, analyzed or prescribed. The very ubiquitousness of other people in our lives poses a tantalizing challenge to the computational modeler: people are at once the most common object of interest and yet the most structurally complex. Their everyday movements are amazingly uid yet demanding to reproduce, with actions driven not just mechanically by muscles and bones but also cognitively by beliefs and intentions. Our motor systems manage to learn how to make us move without leaving us the burden or pleasure of knowing how we did it. Likewise we learn how to describe the actions and behaviors of others without consciously struggling with the processes of perception, recognition, and language

Four-bar linkage synthesis using non-convex optimization

Ce mémoire présente une méthode pour synthétiser automatiquement des mécanismes articulés à quatre barres. Un logiciel implémentant cette méthode a été développé dans le cadre d’une initiative d’Autodesk Research portant sur la conception générative. Le logiciel prend une trajectoire en entrée et calcule les paramètres d’un mécanisme articulé à quatre barres capable de reproduire la même trajectoire. Ce problème de génération de trajectoire est résolu par optimisation non-convexe. Le problème est modélisé avec des contraintes quadratiques et des variables réelles. Une contrainte redondante spéciale améliore grandement la performance de la méthode. L’expérimentation présentée montre que le logiciel est plus rapide et précis que les approches existantes.This thesis presents a method to automatically synthesize four-bar linkages. A software implementing the method was developed in the scope of a generative design initiative at Autodesk. The software takes a path as input and computes the parameters of a four-bar linkage able to replicate the same path. This path generation problem is solved using non-convex optimization. The problem is modeled with quadratic constraints and real variables. A special redundant constraint greatly improves the performance of the method. Experiments show that the software is faster and more precise than existing approaches

Spherical frame projections for visualising joint range of motion, and a complementary method to capture mobility data

Quantifying joint range of motion (RoM), the reachable poses at a joint, has many applications in research and clinical care. Joint RoM measurements can be used to investigate the link between form and function in extant and extinct animals, to diagnose musculoskeletal disorders and injuries or monitor rehabilitation progress. However, it is difficult to visually demonstrate how the rotations of the joint axes interact to produce joint positions. Here, we introduce the spherical frame projection (SFP), which is a novel 3D visualisation technique, paired with a complementary data collection approach. SFP visualisations are intuitive to interpret in relation to the joint anatomy because they ‘trace’ the motion of the coordinate system of the distal bone at a joint relative to the proximal bone. Furthermore, SFP visualisations incorporate the interactions of degrees of freedom, which is imperative to capture the full joint RoM. For the collection of such joint RoM data, we designed a rig using conventional motion capture systems, including live audio-visual feedback on torques and sampled poses. Thus, we propose that our visualisation and data collection approach can be adapted for wide use in the study of joint function