Sports professionals have been increasingly using Virtual Reality (VR) for training and assessment of skill-based sports. Yet fundamental questions about the virtue of VR training for skill-based sports remain unanswered: Can the complex motor skills in these sports be learned in VR? If so, do these skills transfer to the real world? We have developed a VR table tennis system that incorporates customized physics with realistic audio-visual stimuli, haptics, and motion capture to enhance VR immersion and collect information about the player's posture and technique. We have assessed skill acquisition and training transfer by comparing real table tennis performance between a control group (n=7) that received no training and an experimental group (n=8) trained for five sessions in VR. Results show a significant improvement in technique but no significant changes in the number of the returned balls in the experimental group in the real-life retention session. However, no significant differences are found in the control group. Our findings support the notion that complex skills can be learned in VR and that obtained skills can transfer to the real world. This work offers an inexpensive VR table tennis training platform, enabling effective training via real-time motor and ball returning technique feedback.

The fusion of multiple imaging modalities presents an important contribution to machine vision, but remains an ongoing challenge due to the limitations in traditional calibration methods that perform a single, global alignment. For depth and thermal imaging devices, sensor and lens intrinsics (FOV, resolution, etc.) may vary considerably, making per-pixel fusion accuracy difficult. In this paper, we present AccuFusion, a two-phase non-linear registration method to fuse multimodal images at a per-pixel level to obtain an efficient and accurate image registration. The two phases: the Coarse Fusion Network (CFN) and Refining Fusion Network (RFN), are designed to learn a robust image-space fusion that provides a non-linear mapping for accurate alignment. By employing the refinement process, we obtain per-pixel displacements to minimize local alignment errors and observe an increase of 18% in average accuracy over global registration.

Procedural generation of elastic structures provides the fundamental basis for controlling and designing 3D printed deformable object behaviors. The automation through generative algorithms provides flexibility in how design and functionality can be seamlessly integrated into a cohesive process that generates 3D prints with variable elasticity. Generative deformation introduces an automated method for perforating existing volumetric structures, promoting simulated deformations, and integrating stress analysis into a cohesive pipeline model that can be used with existing consumer-level 3D printers with elastic material capabilities. In this work, we present a consolidated implementation of the design, simulate, refine, and 3D print procedure based on the automated generation of heterogeneous lattice structures. We utilize Finite Element Analysis (FEA) metrics to generate perforated deformation models that adhere to deformation behaviors created within our design environment. We present the core algorithms, automated pipeline, and 3D print deformations of various objects. Quantitative results illustrate how the heterogeneous geometric structure can influence elastic material behaviors towards design objectives. Our method provides an automated open-source tool for quickly prototyping elastic 3D prints.

The ability to detect and diagnose neurocognitive disorders at the earliest possible moment is key to a better prognosis for the patient. Two of the earliest indicators of potential neurocognitive problems are motor and visual dysfunction. Motor disorders and problems in visual cognition can be seen in many neurocognitive disorders, resulting in abnormal physical reactions to visual stimuli. Analyzing physical behaviors when presented with such stimuli can provide insights into the visual perception and motor abilities of an individual, yet there is currently no unbiased, objective, general-purpose tool that analyzes attention and motor behavior to assess neurocognitive function. We propose a novel method of neurocognitive function assessment that tests the patient's cognition using virtual reality with eye tracking and motion analysis. By placing the patient in a controlled virtual environment and analyzing their movements, we can evoke certain physical responses from subjects for neurocognitive assessment. We have developed a prototype system that places the subject in a virtual baseball field and captures their full body motion as they try to catch baseballs. This scenario tests the subject's ability to determine the landing time and position of the ball, as well as the test subject's balance, motor skills, attention, and memory. Preliminary tests with 20 healthy normal individuals demonstrate the ability of this tool to assess the test subject's balance, memory, attention, and reaction to visual stimuli. This platform has a twofold contribution: it is used to assess several neurocognitive constructs that affect visual and motor capability neutrally and objectively based on controlled stimuli, and it enables objective comparison between different neurocognitive disorders research in this field.

Stroke is a leading cause of long-term impairment, causing a fatality if not act upon in time. Home-based post-stroke rehabilitation plays an important role in helping patients to regain normal mobility and functionality at their residence. However, existing home-based rehabilitation approaches fail to effectively motivate patients on frequent engagement with exercise to achieve the intended outcome. In this paper, we develop VRInsole, a synthetical solution combining a Smart Insole footwear sensor and virtual reality (VR), targeting lower extremity mobility training in an immersive environment for stroke rehabilitation. Specifically, the motion information collected from the Smart Insole serve as the input for the VR to perform corresponding exercise animations. To prove the feasibility of VRInsole, an experiment is conducted on the recognition of lower extremity motion direction, which achieves an average accuracy of 93.9%.

In this paper we present a novel interactive method and interface techniques for controlling the behavior of physically-based simulation of deformable objects. The goal of our research is to provide users an ability to control the motion which appears physically correct, preserves the moving pattern of the original motion, and satisfies goals for a deformable object. In our approach, a user can select any part of the deformable structure, called control points, and can define target poses by moving control points. A user also can define target poses then our system automatically generates the motion path to achieve the target pose. With this technique patient specific organ simulation can be achieved by using a stream of image data. A series of sectional images can be the target poses. The optimal path generator computes the required control parameters that steer the intended node to the desired goal position while preserving the moving pattern of the original motion. It guarantees that the edited motion is physically conforming and natural.

Stable and effective enforcement of hard constraints is one of the crucial components in controlling physics-based dynamic simulation systems. The conventional explicit Baumgarte constraint stabilization confines the time step to be within a stability limit and requires users to pick problem-dependent coefficients to achieve fast convergence or to prevent oscillations. The recently proposed post-stabilization method has shown a successful constraint drift reduction but it does not guarantee the physically correct behavior of motion and requires additional computational cost to decrease the constraint errors. This paper presents our new implicit constraint enforcement technique that is stable over large time steps and does not require problem dependent stabilization parameters. This new implicit constraint enforcement method uses the future time step to estimate the correct magnitude of the constraint forces, resulting in better stability over bigger time steps. More importantly, the proposed method generates physically conforming constraint forces while minimizing the constraint drifts, resulting in physically correct motion. Its asymptotic computational complexity is same as the explicit Baumgarte method. It can be easily integrated into various constrained dynamic systems including rigid body or deformable structure applications. This paper describes a formulation of implicit constraint enforcement and an accumulated constraint error and dynamic behavior analysis for comparison with existing methods.

This paper presents a multi-resolutional surface deformable model with physical property adjustment scheme and shape-preserving springs to represent surface-deformable objects efficiently and robustly. In order to reduce the computational complexity while ensuring the same global volumetric behaviour for the deformable object, we introduce a multi-resolutional mass-spring model that is locally refined using the modified-butterfly subdivision scheme. For robust deformation, a shape-preserving spring, which helps to restore the model to the original shape, is proposed to reduce the animation instability. Volume and shape preservation is indirectly achieved by restoring the model to the original shape without computing the actual volume and associated forces at every iteration. Most existing methods concentrate on visual realism of multi-resolutional deformation and often neglect to maintain the dynamic behavioural integrity between detail levels. In order to preserve overall physical behaviour, we present a new scheme for adjusting physical properties between different levels of details. During the animation of deformable objects, the part of the object under external forces beyond a threshold or with large surface curvature variations is refined with a higher level of detail. The physical properties of nodes and springs in the locally refined area are adjusted in order to preserve the total mass and global behaviour of the object. The adequacy of the proposed scheme was analysed with tests using practical mesh examples. Experimental results demonstrate improved efficiency in object deformation and preservation of overall behaviour between different levels. Copyright © 2005 John Wiley & Sons, Ltd.

This paper describes formulation and management of constraints, and a nonlinear optimization algorithm that together enable interactive geometrically aware manipulation of articulated objects. Going beyond purely kinematic or dynamic approaches, our solution method directly employs geometric constraints to ensure non‐interpenetration during object manipulation. We present the formulation of the inequality constraints used to ensure nonpenetration, describe how to manage the set of active inequality constraints as objects move, and show how these results are combined with a nonlinear optimization algorithm to achieve interactive geometrically aware object manipulation. Our optimization algorithm handles equality and inequality constraints and does not restrict object topology. It is an efficient iterative algorithm, quadratically convergent, with each iteration bounded by O(nnz(L)), where nnz(L) is the number of non‐zeros in L, a Cholesky factor of a sparse matrix.

Techniques for interactive 3D manipulation of articulated objects in cluttered environments should be geometrically aware, going beyond basic inverse or forward kinematics to allow contact while preventing interpenetration. This paper describes a general purpose interactive object manipulation technique using nonlinear optimization. The method converts geometry awareness into sets of inequality constraints and handles nonlinear equality and inequality constraints efficiently without restricting object topology. Our iterative algorithm has a quadratic convergence rate and each iteration can be solved in O(n/sub nz/(L)), where n/sub nz/(L) is the number of non-zeros in L, a Cholesky factor of a sparse matrix. To promote additional speedup, symbolic factorization is separated from numerical computation. Our approach provides a framework for using optimization techniques in interactive tools for building and manipulating models in constrained, cluttered environments.