Identifying Relationships between Physiological Measures and Evaluation Metrics for 3D Interaction Techniques

Analysis of raw volume data generated from different scanning technologies faces a variety of challenges, related to search, pattern recognition, spatial understanding, quantitative estimation, and shape description. In a previous study, we found that the Volume Cracker (VC) 3D interaction (3DI) technique mitigated some of these problems, but this result was from a tethered glove-based system with users analyzing simulated data. Here, we redesigned the VC by using untethered bare-hand interaction with real volume datasets, with a broader aim of adoption of this technique in research labs. We developed symmetric and asymmetric interfaces for the Bare-Hand Volume Cracker (BHVC) through design iterations with a biomechanics scientist. We evaluated our asymmetric BHVC technique against standard 2D and widely used 3D interaction techniques with experts analyzing scanned beetle datasets. We found that our BHVC design significantly outperformed the other two techniques. This study contributes a practical 3DI design for scientists, documents lessons learned while redesigning for bare-hand trackers, and provides evidence suggesting that 3D interaction could improve volume data analysis for a variety of visual analysis tasks. Our contribution is in the realm of 3D user interfaces tightly integrated with visualization, for improving the effectiveness of visual analysis of volume datasets. Based on our experience, we also provide some insights into hardware-agnostic principles for design of effective interaction techniques.

Empirical evidence of the benefits of immersion is an important goal for the virtual environment (VE) community. Direct comparison of immersive systems and non-immersive systems is insufficient because differences between such systems may be due not only to the level of immersion, but also to other factors, such as the input devices and interaction techniques used. In this paper, a study is presented that separates the effects of level of immersion and 3D interaction technique for a six-degree-of-freedom manipulation task. In the study, two components of immersion -- stereoscopy and field of regard -- were varied and three 3D interaction techniques -- HOMER, Go-Go, and DO-IT (a new keyboard- and mouse-based technique) -- were tested. The results of the experiment show that the interaction technique had a significant effect on object manipulation time, while the two components of immersion did not. The implications of these results are discussed for VE application developers.

In this paper, we propose tools for studying biophysical characteristics of a person by processing polygraph charts. We present the results produced by two methods of multivariate data analysis: the principal component analysis (PCA) and projection on latent structures (PLS). Data of biophysical state of a person during the polygraph testing - galvanic skin response (GSP), photoplethysmogram, blood pressure, and other biophysical characteristics – are processed and analyzed.It is shown that load diagrams are the preferable method to identify the relationship between the biophysical characteristics of a person. The principal component analysis provides an opportunity to assess minor changes in biophysical characteristics of a person and to take into account situational significant identifiers. They can attest to the fact that a possibly significant physical-chemical process runs in a human body.It is demonstrated that the projection on latent structures should be used to develop regression models that can constantly be used in polygraph testing.It is concluded that proposed methods of multivariate data analysis can help in an investigation of biophysical characteristics of a person using polygraph data. Also, the proposed methods are able to detect possible malfunctions in the responsible physical-chemical processes. The obtained results enable solving practical problems of information security matters, for example, the identification of an insider.DOI 10.14258/izvasu(2018)1-05

In this paper, we present a study to show that matching the dimensionality of interaction techniques with the dimensional demands of the task results in an interface that facilitates superior performance on interaction tasks without sacrificing performance on 2D tasks in favor of 3D tasks and vice versa. We describe the concept of dimensional congruence and how to identify the dimensional characteristics of a task so that appropriate interaction techniques can be applied. We constructed a prototypical application in a Virtual Environment Enclosure (VEE) using a hand-held device to show how this might be done in this type of apparatus. We then describe a study that evaluates both 2D and 3D tasks as performed using typical 2D and 3D interaction techniques. Results show that an appropriate mix of 2D and 3D interaction techniques is preferred over exclusive use of one or the other. The challenge lies not in selecting independent interaction techniques for specific tasks, but rather in constructing an overall interface that mixes 2D and 3D interactions appropriately.

The performance of neural networks in classifying mass spectral data is evaluated and compared to methods of multivariate data analysis and pattern recognition. Back propagation networks are matched with linear discriminant analysis, Kohonen feature maps are compared to the knearest neighbour clustering algorithm. Eight classifiers were trained, in order to discriminate mass spectra of steroids from eight distinct classes of chemical compounds. The results obtained show slightly better performance of Kohonen networks compared to k-nearest neighbour clustering and equal performance of multi-layer perceptrons and discriminant analysis.

Real-time 3D interaction with augmented reality environments is one of the main features of any AR system. However, selecting and manipulating distal 3D virtual objects in AR still suffer from lack of accuracy and precision. In this paper, we propose an alternate 3D interaction technique called for selection and manipulation distant objects in immersive video see-through augmented reality. Zoom-in interaction technique is based on the idea of zooming the captured images. This allows bringing closer both of real and virtual distant objects, while keeping the spatial registration between the virtual and the real scenes thanks to a robust real-time computer vision algorithm for pose estimation. An evaluation and comparison with other well-known technique are given at the end of this paper, in order to validate our proposed approach.

Balloon selection is a 3D interaction technique that is modeled after the real world metaphor of manipulating a helium balloon attached to a string. Balloon selection allows for precise 3D selection in the volume above a tabletop surface by using multiple fingers on a multi-touch-sensitive surface. The 3DOF selection tasks is decomposed in part into a 2DOF positioning task performed by one finger on the tabletop in an absolute 2D Cartesian coordinate system and a 1DOF positioning task performed by another finger on the tabletop in a relative 2D polar coordinate system. We have evaluated balloon selection in a formal user study that compared it to two well-known interaction techniques for selecting a static 3D target: a 3DOF tracked wand and keyboard cursor keys. We found that balloon selection was significantly faster than using cursor keys and had a significantly lower error rate than the wand. The lower error rate appeared to result from the user's hands being supported by the tabletop surface, resulting in significantly reduced hand tremor and arm fatigue.

Impact of multi-dimensional cognitive demands on takeover performance, physiological and eye-tracking measures in conditionally automated vehicles

Robotics holds the potential to streamline the execution of repetitive and dangerous tasks, which are difficult or impossible for a human operator. However, in complex scenarios, such as nuclear waste management or disaster response, full automation often proves unfeasible due to the diverse and intricate nature of tasks, coupled with the unpredictable hazards, and is typically prevented by stringent regulatory frameworks. Consequently, the predominant approach to managing activities in such settings remains human teleoperation. Teleoperation can be demanding, especially in high-stress situations, and involves a complex blend of both cognitive and physical workload. We present an experiment to explore a range of physiological and performance-related metrics for workload assessment during robotic teleoperation. Thirty-five participants performed a teleoperation task, during which we manipulated cognitive and physical workload conditions. We recorded multiple metrics, including brain activity using functional Near-Infrared Spectroscopy, galvanic skin responses, cardiovascular responses, subjective workload ratings, task and robot performance data. Our results suggest that robotic teleoperation performance may be the most robust metric for distinguishing between different levels of workload experienced during teleoperation, with most physiological measures becoming insignificant to distinguish high cognitive workload.

Psychophysiology of children with autism spectrum disorder

Variation patterns of nitric oxide in Catalonia during the period from 2001 to 2006 using multivariate data analysis methods

Multivariate data analysis methods, although widely diffused since the 1960’s, are really much older. Hotelling founded both the Principal Component Analysis and the Canonical Analysis in the 30’s, by further developing previous studies made by Spearman and Pearson at the beginning of the century. These methods were subsequently expanded and enriched, but remained beyond the reach of the common practitioner because of the massive calculations they required. With the birth, and subsequent dramatic development of computers, multivariate data analysis methods have finally become practicable (Bouroche and Saporta, 1983).KeywordsFactor ScoremUltivariate MethodMultivariate Data AnalysisAmes TestHydrazine SulfateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper aims to raise the question : ”How much 3D bimanual interaction techniques can be useful to the design of collaborative interaction techniques in the field of Collaborative Virtual Environment (CVE)?”. Indeed, CVE involve the use of complex interaction techniques based on specific collaborative metaphors. The design of these metaphors may be a difficult task because it has to deal with collaborative issues that came from sparse research areas (Human-Computer Interfaces, Human-Human Interactions, Networking, Physiology and Social Psychology). Metaphors for bimanual interactions have been developed for a while essentially because it is a widely spread area of interest for common tasks. Bimanual interactions involve the use of both hands of the user in a collaborative way in order to achieve a goal with better performances compared to uni-manual interactions thanks to a natural skill that is proprioception. This collaborative aspect could certainly be a helpful entry point in the design of efficient collaborative interaction techniques extended from improved bimanual metaphors. However, the proprioceptive sense cannot be considered in the same way, and additional features must be proposed to be able to collaborate efficiently. Thus, awareness is a key to let CVE be usable and the availability of collaborative feedbacks is essential to extend bimanual interactions toward collaborative ones. In this paper, we based our study on existing work on bimanual and collaborative interaction techniques trying to draw similarities between them. We emphasize common points between both fields that could be useful to better design both metaphors and awareness in CVE.

This paper describes a novel 3D interaction technique called the SkeweR, dedicated to the 2-user collaborative manipulation of objects in virtual environments. This technique enables two users to move simultaneously the same virtual object in 3D. For this aim, each user manipulates the object by one crushing point, like handling the extremity of a skewer. The SkeweR uses only translation information from the users’ motions to change both the position and orientation of the manipulated object. By using more crushing points, this technique could easily be extended to 3 or more users. Thus, the SkeweR technique could be used to improve the collaborative manipulation of objects in numerous applications of Virtual Reality, such as: virtual prototyping, maintenance and training simulations, architectural mock-up reviews, etc.

We review recent literature in the organizational sciences that uses some form of physiological measurement. We organize our review in terms of the underlying constructs that physiological measures were intended to assess. The majority of such constructs represents stress, health, or arousal, although these constructs are often studied in an attempt to understand a diverse set of other phenomena. The majority of the studies we discuss use peripheral measures of the autonomic nervous system or biological indicators of various physiological subsystems, such as the cardiovascular, metabolic, or immunological. Advances in instrumentation and biological assaying methods have made the use of physiological measures more feasible, and in some cases, affordable for researchers without specialized training in physiology. The significant challenges we discuss mostly concern issues of sampling and timing, as well as the careful selection of physiological indicators to fit the theoretical demands of the research.