1,358 research outputs found
Functionality-Driven Musculature Retargeting
We present a novel retargeting algorithm that transfers the musculature of a
reference anatomical model to new bodies with different sizes, body
proportions, muscle capability, and joint range of motion while preserving the
functionality of the original musculature as closely as possible. The geometric
configuration and physiological parameters of musculotendon units are estimated
and optimized to adapt to new bodies. The range of motion around joints is
estimated from a motion capture dataset and edited further for individual
models. The retargeted model is simulation-ready, so we can physically simulate
muscle-actuated motor skills with the model. Our system is capable of
generating a wide variety of anatomical bodies that can be simulated to walk,
run, jump and dance while maintaining balance under gravity. We will also
demonstrate the construction of individualized musculoskeletal models from
bi-planar X-ray images and medical examinations.Comment: 15 pages, 20 figure
A multimedia package for patient understanding and rehabilitation of non-contact anterior cruciate ligament injuries
Non-contact anterior cruciate ligament (ACL) injury is one of the most common ligament injuries in the body. Many patientsâ receive graft surgery to repair the damage, but have to undertake an extensive period of rehabilitation. However, non-compliance and lack of understanding of the injury, healing process and rehabilitation means patientâs return to activities before effective structural integrity of the graft has been reached. When clinicians educate the patient, to encourage compliance with treatment and rehabilitation, the only tools that are currently widely in use are static plastic models, line diagrams and pamphlets. As modern technology grows in use in anatomical education, we have developed a unique educational and training package for patientâs to use in gaining a better understanding of their injury and treatment plan. We have combined cadaveric dissections of the knee (and captured with high resolution digital images) with reconstructed 3D modules from the Visible Human dataset, computer generated animations, and images to produce a multimedia package, which can be used to educate the patient in their knee anatomy, the injury, the healing process and their rehabilitation, and how this links into key stages of improving graft integrity. It is hoped that this will improve patient compliance with their rehabilitation programme, and better long-term prognosis in returning to normal or near-normal activities. Feedback from healthcare professionals about this package has been positive and encouraging for its long-term use
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical Locomotion
Muscle-actuated control is a research topic that spans multiple domains,
including biomechanics, neuroscience, reinforcement learning, robotics, and
graphics. This type of control is particularly challenging as bodies are often
overactuated and dynamics are delayed and non-linear. It is however a very well
tested and tuned actuation mechanism that has undergone millions of years of
evolution with interesting properties exploiting passive forces and efficient
energy storage of muscle-tendon units. To facilitate research on
muscle-actuated simulation, we release a 3D musculoskeletal simulation of an
ostrich based on the MuJoCo physics engine. The ostrich is one of the fastest
bipeds on earth and therefore makes an excellent model for studying
muscle-actuated bipedal locomotion. The model is based on CT scans and
dissections used to collect actual muscle data, such as insertion sites,
lengths, and pennation angles. Along with this model, we also provide a set of
reinforcement learning tasks, including reference motion tracking, running, and
neck control, used to infer muscle actuation patterns. The reference motion
data is based on motion capture clips of various behaviors that we preprocessed
and adapted to our model. This paper describes how the model was built and
iteratively improved using the tasks. We also evaluate the accuracy of the
muscle actuation patterns by comparing them to experimentally collected
electromyographic data from locomoting birds. The results demonstrate the need
for rich reward signals or regularization techniques to constrain muscle
excitations and produce realistic movements. Overall, we believe that this work
can provide a useful bridge between fields of research interested in muscle
actuation.Comment: https://github.com/vittorione94/ostrichr
Breathing Life Into Biomechanical User Models
Forward biomechanical simulation in HCI holds great promise as a tool for evaluation, design, and engineering of user interfaces. Although reinforcement learning (RL) has been used to simulate biomechanics in interaction, prior work has relied on unrealistic assumptions about the control problem involved, which limits the plausibility of emerging policies. These assumptions include direct torque actuation as opposed to muscle-based control; direct, privileged access to the external environment, instead of imperfect sensory observations; and lack of interaction with physical input devices. In this paper, we present a new approach for learning muscle-actuated control policies based on perceptual feedback in interaction tasks with physical input devices. This allows modelling of more realistic interaction tasks with cognitively plausible visuomotor control. We show that our simulated user model successfully learns a variety of tasks representing different interaction methods, and that the model exhibits characteristic movement regularities observed in studies of pointing. We provide an open-source implementation which can be extended with further biomechanical models, perception models, and interactive environments.publishedVersio
Reinforcement learning control of a biomechanical model of the upper extremity
Among the infinite number of possible movements that can be produced, humans
are commonly assumed to choose those that optimize criteria such as minimizing
movement time, subject to certain movement constraints like signal-dependent
and constant motor noise. While so far these assumptions have only been
evaluated for simplified point-mass or planar models, we address the question
of whether they can predict reaching movements in a full skeletal model of the
human upper extremity. We learn a control policy using a motor babbling
approach as implemented in reinforcement learning, using aimed movements of the
tip of the right index finger towards randomly placed 3D targets of varying
size. We use a state-of-the-art biomechanical model, which includes seven
actuated degrees of freedom. To deal with the curse of dimensionality, we use a
simplified second-order muscle model, acting at each degree of freedom instead
of individual muscles. The results confirm that the assumptions of
signal-dependent and constant motor noise, together with the objective of
movement time minimization, are sufficient for a state-of-the-art skeletal
model of the human upper extremity to reproduce complex phenomena of human
movement, in particular Fitts' Law and the 2/3 Power Law. This result supports
the notion that control of the complex human biomechanical system can plausibly
be determined by a set of simple assumptions and can easily be learned.Comment: 19 pages, 7 figure
Muscle activation mapping of skeletal hand motion: an evolutionary approach.
Creating controlled dynamic character animation consists of mathe- matical modelling of muscles and solving the activation dynamics that form the key to coordination. But biomechanical simulation and control is com- putationally expensive involving complex di erential equations and is not suitable for real-time platforms like games. Performing such computations at every time-step reduces frame rate. Modern games use generic soft- ware packages called physics engines to perform a wide variety of in-game physical e ects. The physics engines are optimized for gaming platforms. Therefore, a physics engine compatible model of anatomical muscles and an alternative control architecture is essential to create biomechanical charac- ters in games. This thesis presents a system that generates muscle activations from captured motion by borrowing principles from biomechanics and neural con- trol. A generic physics engine compliant muscle model primitive is also de- veloped. The muscle model primitive forms the motion actuator and is an integral part of the physical model used in the simulation. This thesis investigates a stochastic solution to create a controller that mimics the neural control system employed in the human body. The control system uses evolutionary neural networks that evolve its weights using genetic algorithms. Examples and guidance often act as templates in muscle training during all stages of human life. Similarly, the neural con- troller attempts to learn muscle coordination through input motion samples. The thesis also explores the objective functions developed that aids in the genetic evolution of the neural network. Character interaction with the game world is still a pre-animated behaviour in most current games. Physically-based procedural hand ani- mation is a step towards autonomous interaction of game characters with the game world. The neural controller and the muscle primitive developed are used to animate a dynamic model of a human hand within a real-time physics engine environment
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