This literature review examines the use of virtual reality in ergonomic risk assessment in workplace design using a rigorous methodology to evaluate literature from seven databases and journals. The review identified 4,377 records, of which nineteen used virtual reality to simulate complex work environments, such as manufacturing floors and office spaces, utilizing motion tracking and risk assessment methods, including the Ergonomic Assessment Worksheet (EAWS) or CUELA (German acronym for Computer-aided recording and long-term analysis of musculoskeletal system loads). The research suggests that integrating virtual reality with established ergonomic assessment methods holds promise for enhancing their efficacy. However, it also highlights the limitations of current virtual reality simulation tools in implementing realistic interactions with the working environment. The importance of this review lies in its capacity to synthesize and structure a previously fragmented field, providing researchers and practitioners with a comprehensive overview of current approaches, challenges, and opportunities in VR-based ergonomic risk assessment. It concludes that virtual reality has high potential but requires further research to fully realize its benefits in designing ergonomic workplaces, conducting thorough ergonomic evaluations, and being integrated into occupational health and safety on a large scale.

Ergonomic Workplace Assessment aims to assess potential risks and measure the ergonomic characteristics of various workplaces. Virtual reality might offer great benefits as it enables cost-effective workplace evaluations in early planning phases. This study aims to investigate the validity of motion-capture-based ergonomic evaluations in virtual environments (VE) compared to real environments (RE), focusing on the role of passive haptics. In a 3\4 design, 22 subjects performed four work tasks in three environmental conditions: RE, VE, and VE with passive haptic feedback (PH). Whole-body motion data were recorded and analyzed using repeated measures ANOVA. Eighteen joint angle percentiles were analyzed across all tasks and environments. Significant differences in trunk and shoulder movements were found between RE and VE, especially during physically demanding tasks such as repetitive lifting. Passive haptics reduced differences in nine task-joint constellations but introduced new ones in seventeen others, particularly shoulder joints. Overall, the results highlight the context-specific effects of passive haptics: while it can improve motion realism in VE, it can also induce task-specific artifacts.

Abstract In this paper we revisit the computation and visualization of equivalents to isocontours in uncertain scalar fields. We model uncertainty by discrete random fields and, in contrast to previous methods, also take arbitrary spatial correlations into account. Starting with joint distributions of the random variables associated to the sample locations, we compute level crossing probabilities for cells of the sample grid. This corresponds to computing the probabilities that the well‐known symmetry‐reduced marching cubes cases occur in random field realizations. For Gaussian random fields, only marginal density functions that correspond to the vertices of the considered cell need to be integrated. We compute the integrals for each cell in the sample grid using a Monte Carlo method. The probabilistic ansatz does not suffer from degenerate cases that usually require case distinctions and solutions of ill‐conditioned problems. Applications in 2D and 3D, both to synthetic and real data from ensemble simulations in climate research, illustrate the influence of spatial correlations on the spatial distribution of uncertain isocontours.

In this paper we address the difficult problem of parameter-finding in image segmentation. We replace a tedious manual process that is often based on guess-work and luck by a principled approach that systematically explores the parameter space. Our core idea is the following two-stage technique: We start with a sparse sampling of the parameter space and apply a statistical model to estimate the response of the segmentation algorithm. The statistical model incorporates a model of uncertainty of the estimation which we use in conjunction with the actual estimate in (visually) guiding the user towards areas that need refinement by placing additional sample points. In the second stage the user navigates through the parameter space in order to determine areas where the response value (goodness of segmentation) is high. In our exploration we rely on existing ground-truth images in order to evaluate the "goodness" of an image segmentation technique. We evaluate its usefulness by demonstrating this technique on two image segmentation algorithms: a three parameter model to detect microtubules in electron tomograms and an eight parameter model to identify functional regions in dynamic Positron Emission Tomography scans.

Abstract We present an approach for extracting extremal feature lines of scalar indicators on surface meshes, based on discrete Morse Theory. By computing initial Morse‐Smale complexes of the scalar indicators of the mesh, we obtain a candidate set of extremal feature lines of the surface. A hierarchy of Morse‐Smale complexes is computed by prioritizing feature lines according to a novel criterion and applying a cancellation procedure that allows us to select the most significant lines. Given the scalar indicators on the vertices of the mesh, the presented feature line extraction scheme is interpolation free and needs no derivative estimates. The technique is insensitive to noise and depends only on one parameter: the feature significance. We use the technique to extract surface features yielding impressive, non photorealistic images.