RESUMEN
Data-driven texture modeling and rendering has pushed the limit of realism in haptics. However, the lack of haptic texture databases, difficulties of model interpolation and expansion, and the complexity of real textures prevent data-driven methods from capturing a large variety of textures and from customizing models to suit specific output hardware or user needs. This work proposes an interactive texture generation and search framework driven by user input. We design a GAN-based texture model generator, which can create a wide range of texture models using Auto-Regressive processes. Our interactive texture search method, which we call "preference-driven," follows an evolutionary strategy given guidance from user's preferred feedback within a set of generated texture models. We implemented this framework on a 3D haptic device and conducted a two-phase user study to evaluate the efficiency and accuracy of our method for previously unmodeled textures. The results showed that by comparing the feel of real and generated virtual textures, users can follow an evolutionary process to efficiently find a virtual texture model that matched or exceeded the realism of a data-driven model. Furthermore, for 4 out of 5 real textures, ≥ 80% of the preference-driven models from participants were rated comparable to the data-driven models.
Asunto(s)
Interfaz Usuario-Computador , Retroalimentación , HumanosRESUMEN
Haptics plays an important role in training users to assemble mechanical components, such as airplane or car parts. Because mechanical components are often geometrically complex, efficient collision detection and six-DoF haptic rendering of contact are required for virtual assembly, and this has been extensively explored in prior work. However, as this article shows, this alone is not sufficient for efficient virtual assembly training. This article asks how to augment six-DoF haptic rendering of contact to maximize virtual assembly training efficiency, and proposes and measures several visual and haptic guidance strategies. Our visual strategies consist of displaying animations of the correct assembly path, motion indicator cues, and close-ups on difficult assembly path sections. Our haptic guidance consists of forces and torques that correct the trainee's deviation from the path. We investigate several haptic guidance strategies, including continuous forces and torques, force/torque nudging and anti-forces/torques. We designed a user study to evaluate the training efficiency of our proposed strategies quantitatively, using ANOVA and Tukey statistics. Our main finding is that the most efficient training approach is to use haptic rendering of contact in combination with visual animation-based guidance. Continuous forces, nudging, anti-forces and motion indicator cues were measured to be less effective.
Asunto(s)
Fenómenos Mecánicos , Interfaz Usuario-Computador , Humanos , Movimiento (Física) , TorqueRESUMEN
Haptic-based tissue stiffness perception is essential for palpation training system, which can provide the surgeon haptic cues for improving the diagnostic abilities. However, current haptic devices, such as Geomagic Touch, fail to provide immersive and natural haptic interaction in virtual surgery due to the inherent mechanical friction, inertia, limited workspace and flawed haptic feedback. To tackle this issue, we design a novel magnetic levitation haptic device based on electromagnetic principles to augment the tissue stiffness perception in virtual environment. Users can naturally interact with the virtual tissue by tracking the motion of magnetic stylus using stereoscopic vision so that they can accurately sense the stiffness by the magnetic stylus, which moves in the magnetic field generated by our device. We propose the idea that the effective magnetic field (EMF) is closely related to the coil attitude for the first time. To fully harness the magnetic field and flexibly generate the specific magnetic field for obtaining required haptic perception, we adopt probability clouds to describe the requirement of interactive applications and put forward an algorithm to calculate the best coil attitude. Moreover, we design a control interface circuit and present a self-adaptive fuzzy proportion integration differentiation (PID) algorithm to precisely control the coil current. We evaluate our haptic device via a series of quantitative experiments which show the high consistency of the experimental and simulated magnetic flux density, the high accuracy (0.28 mm) of real-time 3D positioning and tracking of the magnetic stylus, the low power consumption of the adjustable coil configuration, and the tissue stiffness perception accuracy improvement by 2.38 percent with the self-adaptive fuzzy PID algorithm. We conduct a user study with 22 participants, and the results suggest most of the users can clearly and immersively perceive different tissue stiffness and easily detect the tissue abnormality. Experimental results demonstrate that our magnetic levitation haptic device can provide accurate tissue stiffness perception augmentation with natural and immersive haptic interaction.
Asunto(s)
Elasticidad/fisiología , Palpación , Procesamiento de Señales Asistido por Computador/instrumentación , Cirujanos/educación , Realidad Virtual , Adulto , Algoritmos , Fenómenos Biomecánicos/fisiología , Diseño de Equipo , Retroalimentación , Femenino , Humanos , Riñón/fisiología , Riñón/cirugía , Campos Magnéticos , Masculino , Modelos Biológicos , Fantasmas de ImagenRESUMEN
The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young's modulus and Poisson's ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg-Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03â¯Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5â¯mm, demonstrating that our nonlinear parameters are precise.