A fast and robust Six-DoF god object heuristic for haptic rendering of complex models with friction

Abstract The most accurate haptic rendition of a virtual model is produced when the haptic algorithm acts directly on the actual model and not an intermediate representation. In the modeling and design communities the de facto model representation standard is NURBS. Further, more powerful systems provide trimming and adjacency information within the models representation. This additional information permits more complex models to be expressed succinctly but also increases the complexity of representation. In this paper we present an algorithm that supports direct haptic rendering of models constructed from trimmed NURBS surfaces. Our distributed system links an advanced modeling system to a force-reflecting device. In addition, we present extensions to the algorithm which support model manipulation, dimensioned probes, and multi-probe contact.

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A Prediction Method Using Interpolation for Smooth Six-DOF Haptic Rendering in Multirate Simulation

2013 International Conference on Cyberworlds ◽

10.1109/cw.2013.43 ◽

2013 ◽

Cited By ~ 2

Author(s):

Xiyuan Hou ◽

Olga Sourina

Keyword(s):

Prediction Method ◽

Haptic Rendering ◽

Six Dof

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Further Insights Into the German Version of the Multidimensional Assessment of Interoceptive Awareness (MAIA)

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000404 ◽

2019 ◽

Vol 35 (3) ◽

pp. 317-325 ◽

Cited By ~ 6

Author(s):

Dorota Reis

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Equation Modeling ◽

Interoceptive Awareness ◽

Multidimensional Assessment ◽

New Instrument ◽

Confirmatory Factor ◽

Complex Models ◽

Approximate Zero ◽

Bayesian Structural Equation Modeling

Abstract. Interoception is defined as an iterative process that refers to receiving, accessing, appraising, and responding to body sensations. Recently, following an extensive process of development, Mehling and colleagues (2012) proposed a new instrument, the Multidimensional Assessment of Interoceptive Awareness (MAIA), which captures these different aspects of interoception with eight subscales. The aim of this study was to reexamine the dimensionality of the MAIA by applying maximum likelihood confirmatory factor analysis (ML-CFA), exploratory structural equation modeling (ESEM), and Bayesian structural equation modeling (BSEM). ML-CFA, ESEM, and BSEM were examined in a sample of 320 German adults. ML-CFA showed a poor fit to the data. ESEM yielded a better fit and contained numerous significant cross-loadings, of which one was substantial (≥ .30). The BSEM model with approximate zero informative priors yielded an excellent fit and confirmed the substantial cross-loading found in ESEM. The study demonstrates that ESEM and BSEM are flexible techniques that can be used to improve our understanding of multidimensional constructs. In addition, BSEM can be seen as less exploratory than ESEM and it might also be used to overcome potential limitations of ESEM with regard to more complex models relative to the sample size.

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Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

10.31234/osf.io/4su2j ◽

2019 ◽

Author(s):

Amanda Kay Montoya ◽

Andrew F. Hayes

Keyword(s):

Confidence Intervals ◽

Indirect Effect ◽

Mediation Analysis ◽

Analytic Approach ◽

Analytic Framework ◽

Hypothesis Tests ◽

Bootstrap Confidence Intervals ◽

Complex Models ◽

Path Analytic ◽

Statistical Mediation Analysis

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.

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