Growing the use of Virtual Worlds in education : an OpenSim perspective

The growth in the range of disciplines that Virtual Worlds support for educational purposes is evidenced by recent applications in the fields of cultural heritage, humanitarian aid, space exploration, virtual laboratories in the physical sciences, archaeology, computer science and coastal geography. This growth is due in part to the flexibility of OpenSim, the open source virtual world platform which by adopting Second Life protocols and norms has created a de facto standard for open virtual worlds that is supported by a growing number of third party open source viewers. Yet while this diversity of use-cases is impressive and Virtual Worlds for open learning are highly popular with lecturers and learners alike immersive education remains an essentially niche activity. This paper identifies functional challenges in terms of Management, Network Infrastructure, the Immersive 3D Web and Programmability that must be addressed to enable the wider adoption of Open Virtual Worlds as a routine learning technology platform. We refer to specific use-cases based on OpenSim and abstract generic requirements which should be met to enable the growth in use of Open Virtual Worlds as a mainstream educational facility. A case study of a deployment to support a formal education curriculum and associated informal learning is used to illustrate key points.Postprin

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions.

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations

Self-regulated learning in virtual worlds – an exploratory study in OpenSim

Computer technologies are increasingly used in education to give the student more autonomy, referred to as student centred learning. One of the assumptions often made in this situation is that students will self-regulate to ensure they achieve the intended learning outcomes. Learning in immersive environments is popular as they are engaging, entertaining and flexible. However, a potential tension exists between configuring a multi-user environment to prohibit actions that can disrupt learning and maintaining the freedom and flexibility that generates learner engagement. This research investigates the importance of student self-regulation for learning in OpenSim. The outcome suggests self-regulation is one of the most important factors needed for successful learning within OpenSim as it preserves engagement while dissuading disruptive behaviour. Moreover, the need for suitable user support is identified as key for promoting student self-regulation within OpenSim

Simulating control of the ankle joint

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 33).Computing environments such as Matlab that are conventionally used to simulate dynamics of rigid body systems can be used to model interactions between the system and its environment. However, creating these simulations using Matlab or an equivalent is difficult and there is a need for a more convenient simulation environment for such problems. Two alternative programs, PyODE and OpenSim, were explored to evaluate their ability to fill this need. Models and simulations of the human ankle were created in PyODE. This program is useful for creating simple models where the programmer desires a high level of control over model parameters. Simulations of the ankle kicking a ball and taking a step were created to examine the effect of joint stiffness on these motions and help determine the usefulness of ODE as a simulation tool. Pre-existing models were analyzed in OpenSim. OpenSim is specifically designed for analyzing biomechanical systems. It allows for more complex models to be created but the user has more limited control over the model parameters.by Rebecca Vasquez.S.B

+SPACES: Serious Games for Role-Playing Government Policies

The paper explores how role-play simulations can be used to support policy discussion and refinement in virtual worlds. Although the work described is set primarily within the context of policy formulation for government, the lessons learnt are applicable to online learning and collaboration within virtual environments. The paper describes how the +Spaces project is using both 2D and 3D virtual spaces to engage with citizens to explore issues relevant to new government policies. It also focuses on the most challenging part of the project, which is to provide environments that can simulate some of the complexities of real life. Some examples of different approaches to simulation in virtual spaces are provided and the issues associated with them are further examined. We conclude that the use of role-play simulations seem to offer the most benefits in terms of providing a generalizable framework for citizens to engage with real issues arising from future policy decisions. Role-plays have also been shown to be a useful tool for engaging learners in the complexities of real-world issues, often generating insights which would not be possible using more conventional techniques

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

Design of a Passive Exoskeleton Spine

In this thesis, a passive exoskeleton spine was designed and evaluated by a series of biomechanics simulations. The design objectives were to reduce the human operator’s back muscle efforts and the intervertebral reaction torques during a full range sagittal plane spine flexion/extension. The biomechanics simulations were performed using the OpenSim modeling environment. To manipulate the simulations, a full body musculoskeletal model was created based on the OpenSim gait2354 and “lumbar spine” models. To support flexion and extension of the torso a “push-pull” strategy was proposed by applying external pushing and pulling forces on different locations on the torso. The external forces were optimized via simulations and then a physical exoskeleton prototype was built to evaluate the “push-pull” strategy in vivo. The prototype was tested on three different subjects where the sEMG and inertial data were collected to estimate the muscle force reduction and intervertebral torque reduction. The prototype assisted the users in sagittal plane flexion/extension and reduced the average muscle force and intervertebral reaction torque by an average of 371 N and 29 Nm, respectively

Algorithmic differentiation improves the computational efficiency of OpenSim-based trajectory optimization of human movement

Algorithmic differentiation (AD) is an alternative to finite differences (FD) for evaluating function derivatives. The primary aim of this study was to demonstrate the computational benefits of using AD instead of FD in OpenSim-based trajectory optimization of human movement. The secondary aim was to evaluate computational choices including different AD tools, different linear solvers, and the use of first- or second-order derivatives. First, we enabled the use of AD in OpenSim through a custom source code transformation tool and through the operator overloading tool ADOL-C. Second, we developed an interface between OpenSim and CasADi to solve trajectory optimization problems. Third, we evaluated computational choices through simulations of perturbed balance, two-dimensional predictive simulations of walking, and three-dimensional tracking simulations of walking. We performed all simulations using direct collocation and implicit differential equations. Using AD through our custom tool was between 1.8 ± 0.1 and 17.8 ± 4.9 times faster than using FD, and between 3.6 ± 0.3 and 12.3 ± 1.3 times faster than using AD through ADOL-C. The linear solver efficiency was problem-dependent and no solver was consistently more efficient. Using second-order derivatives was more efficient for balance simulations but less efficient for walking simulations. The walking simulations were physiologically realistic. These results highlight how the use of AD drastically decreases computational time of trajectory optimization problems as compared to more common FD. Overall, combining AD with direct collocation and implicit differential equations decreases the computational burden of trajectory optimization of human movement, which will facilitate their use for biomechanical applications requiring the use of detailed models of the musculoskeletal system.Postprint (published version

Learning to Ascend Stairs and Ramps:Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

This paper proposes to use deep reinforcement learning to teach a physics-based human musculoskeletal model to ascend stairs and ramps. The deep reinforcement learning architecture employs the proximal policy optimization algorithm combined with imitation learning and is trained with experimental data of a public dataset. The human model is developed in the open-source simulation software OpenSim, together with two objects (i.e., the stairs and ramp) and the elastic foundation contact dynamics. The model can learn to ascend stairs and ramps with muscle forces comparable to healthy subjects and with a forward dynamics comparable to the experimental training data, achieving an average correlation of 0.82 during stair ascent and of 0.58 during ramp ascent across both the knee and ankle joints