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Data-driven modeling and rendering is a general approach in haptics aiming to provide highly accurate haptic perceptual experiences simulating complex real physical dynamics, such as deformable or textured objects. A prevalent problem in the present methods for data-driven haptics is that the computational cost for modeling grows rapidly, even becoming intractable, as the interaction complexity or the number of data increases. This paper proposes one data-driven method featured with greatly improved computational efficiency for modeling viscoelastic deformable objects. This advantage is enabled by the use of fractional derivatives for modeling features and regression forests for data-interpolation models. For the benchmark of normal interaction on deformable objects, we describe a computational framework for data-driven haptic modeling and rendering. Its performance is validated by physical experiments for modeling accuracy and cost and a perceptual experiment for the similarity between real and virtual objects. The experiments demonstrate that our method offers highly realistic haptic perceptual experiences with markedly better modeling cost (at least ten times) than other state-of-the-art methods.

BackgroundSocial anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. ObjectiveThis study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. MethodsThis study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. ResultsThe severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. ConclusionsThis study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias. Trial RegistrationClinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508

Five diffusion models are estimated using three different foreign exchange rates to find an appropriate model for each. Daily spot exchange rates expressed as the prices of 1 euro, 1 British pound and 100 Japanese yen in US dollars, respectively denoted by USD/EUR, USD/GBP, and USD/100JPY, are used. The maximum likelihood estimation method is implemented after deriving an approximate log-transition density function (log-TDF) of the diffusion processes because the true log-TDF is unknown. Of the five models, the most general model is the best fit for the USD/GBP, and USD/100JPY exchange rates, but it is not the case for the case of USD/EUR. Although we could not find any evidence of the mean-reverting property for the USD/EUR exchange rate, the USD/GBP, and USD/100JPY exchange rates show the mean-reversion behavior. Interestingly, the volatility function of the USD/EUR exchange rate is increasing in the exchange rate while the volatility functions of the USD/GBP and USD/100Yen exchange rates have a U-shape. Our results reveal that more care has to be taken when determining a diffusion model for the exchange rate. The results also imply that we may have to use a more general diffusion model than those proposed in the literature when developing economic theories for the behavior of the exchange rate and pricing foreign currency options or derivatives.

We present a hybrid framework for the modeling and rendering of both homogeneous and inhomogeneous textures with high realism by combining force and vibration feedback. Given a real texture sample, we first capture a force-feedback model using photometric stereo, which is one of the most accurate algorithms for 3D surface reconstruction in computer vision. This method allows us to model the micro-geometry of the surface and represent it by a two-dimensional (2D) height map in a very high resolution to the order of 10$\mu \text{m}$ . We also provide a new force rendering algorithm adequate for such an accurate and fine texture model. By a user study, we verify that the proposed force modeling and rendering algorithms are suitable for our hybrid texture framework. Second, we model the homogeneous and stationary vibrational characteristics of the real texture sample using the prevalent LPC (linear predictive coding)-based method. Third, we merge the force and vibration feedback models appropriately into a hybrid framework on the basis of their spectral characteristics. Last, another user study that assessed the perceived similarity between real textures and their virtual simulations demonstrates that our hybrid framework can achieve a high level of realism. To our knowledge, our framework is unique in that it can model inhomogeneous (in additional to homogeneous) real textures and render them in a virtual environment with high realism by providing both force and vibrational sensory cues.

The use of distinguishable complex vibrations that have multiple spectral components can improve the transfer of information by vibrotactile interfaces. We investigated the qualitative characteristics of dual-frequency vibrations as the simplest complex vibrations compared to single-frequency vibrations. Two psychophysical experiments were conducted to elucidate the perceptual characteristics of these vibrations by measuring the perceptual distances among single-frequency and dual-frequency vibrations. The perceptual distances of dual-frequency vibrations between their two frequency components along their relative intensity ratio were measured in Experiment I. The estimated perceptual spaces for three frequency conditions showed non-linear perceptual differences between the dual-frequency and single-frequency vibrations. A perceptual space was estimated from the measured perceptual distances among ten dual-frequency compositions and five single-frequency vibrations in Experiment II. The effect of the component frequency and the frequency ratio was revealed in the perceptual space. In a percept of dual-frequency vibration, the lower frequency component showed a dominant effect. Additionally, the perceptual difference among single-frequency and dual-frequency vibrations were increased with a low relative difference between two frequencies of a dual-frequency vibration. These results are expected to provide a fundamental understanding about the perception of complex vibrations to enrich the transfer of information using vibrotactile stimuli.