IEEE Transactions on Haptics最新文献

In spatiotemporal modulation (STM) and lateral modulation (LM) used in conventional mid-air ultrasound tactile stimulation, single or multiple focuses are moved by switching the ultrasound transducer phases. A problem with the phase switching method is the limitation of the focus motion speed due to rapid phase switching that causes sound pressure fluctuations. This paper proposes an LM method using multiple-frequency ultrasound to shift the ultrasound focal point without switching the phase. This method can demonstrate a continuous and stable moving stimulus with high-frequency components, without producing unnecessary audible noise. Using the proposed broadband LM covering up to 400 Hz, we found that a high-frequency 400 Hz LM applied at a finger pad can display a stimulation area with the diameters comparable to or less than the half wavelength of 40 kHz ultrasound, where the perceptual size was evaluated as 4. 2 mm for the long axis diameter and 3. 4 mm for the short axis diameter.

The subcutaneous mechanical response of the fingertip is highly anisotropic due to the presence of a network of collagen fibers linking the outer skin layer to the bone. The impact of this anisotropy on the fingerpad deformation, which had not been studied until now, is here demonstrated using a two-dimensional finite element model of a transverse section of the finger. Different distributions of fiber orientations are considered: radial (physiologic), circumferential, and random (isotropic). The three variants of the model are assessed using experimental observations of a finger pressed on a flat surface. Predictions relying on the physiological orientation of fibers best reproduce experimental trends. Our results show that the orientation of fibers significantly influences the distribution of internal strains and stresses. This leads to a sudden change in the profile of contact pressure when transitioning from sticking to slipping. Interpreted in terms of tactile perception or sensation, these variations might represent important sensory cues for partial slip detection. This is also valuable information for the development of haptic devices.

The objective of this study was to investigate the influence of roughened surface features on the perceived hardness of various materials. Thirteen participants used a visual analog scale to evaluate the hardness of ten 3D-printed specimens by sliding a fingertip on them. The specimens had two types of surface features: flat and smooth, or with microscopic rectangular gratings. They were fabricated from two types of plastic with different Young's moduli-2.46 and 9.35 MPa. We found that both surface pattern and mechanical hardness significantly contributed to the perceived hardness of a material individually and without interaction. The roughened surfaces with rectangular gratings were judged to be harder than the flat and smooth surfaces of the same material. Among the parameters of the rectangular gratings, the groove width or periodic surface wavelength significantly contributed to the perceived hardness. Although the root cause of this phenomenon is unknown, friction caused by surface roughness is considered a potential mediator that influences the perceived hardness. The findings of this study can facilitate the manipulation of softness perception through surface design.

In this survey, we give an overview of hands-free haptic devices specifically designed for navigation guidance while walking. We present and discuss the devices by body part, namely devices for the arm, foot and leg, back, belly and shoulders, waist and finally the head. Although the majority of the experimental tests were successful in terms of reaching the target while being guided by the device, the experimental requirements were wide-ranging. The distances to be covered ranged from just a few meters to more than a kilometer, and while some of the devices worked autonomously, others required the experimenter to act as Wizard of Oz. To compare the usefulness and potential of these devices, we created a table in which we rated several relevant aspects such as autonomy, conspicuity and compactness. Major conclusions are that outdoor devices have the highest technology readiness level, because these allow autonomous navigation through GPS, and that the most compact devices still require the action of an experimenter. Unfortunately, none of the hands-free devices are at a level of readiness where they could be useful to people with visual impairments. The most important factor that should be improved is localization accuracy, which should be high and available at all times.

To design complex wearable haptic interfaces using pressure, we have to explore how we can use pressure stimuli to their full potential. Haptic illusions, such as apparent motion and apparent location, can be a part of this. If these illusions can be evoked with pressure, haptic patterns can increase in complexity without increasing the number of actuators or combining different types of actuators. We did two psychophysical experiments with pressure stimuli on the forearm using a pneumatic sleeve with multiple, individually controlled McKibben actuators. In Experiment 1, we found that spatial integration of two simultaneously presented stimuli occurred for distances up to 61 mm. In Experiment 2, we found that apparent motion can be elicited with distinct pressure stimuli over a range of temporal parameters. These results clearly show spatio-temporal integration in the somatosensory system for pressure stimuli. We discuss these findings in relation to effects found for vibration and the mechanoreceptors in the glabrous skin.

Current Virtual Reality (VR) environments lack the haptic signals that humans experience during real-life interactions, such as the sensation of texture during lateral movement on a surface. Adding realistic haptic textures to VR environments requires a model that generalizes to variations of a user's interaction and to the wide variety of existing textures in the world. Current methodologies for haptic texture rendering exist, but they usually develop one model per texture, resulting in low scalability. We present a deep learning-based action-conditional model for haptic texture rendering and evaluate its perceptual performance in rendering realistic texture vibrations through a multi-part human user study. This model is unified over all materials and uses data from a vision-based tactile sensor (GelSight) to render the appropriate surface conditioned on the user's action in real-time. For rendering texture, we use a high-bandwidth vibrotactile transducer attached to a 3D Systems Touch device. The results of our user study shows that our learning-based method creates high-frequency texture renderings with comparable or better quality than state-of-the-art methods without the need to learn a separate model per texture. Furthermore, we show that the method is capable of rendering previously unseen textures using a single GelSight image of their surface.