IEEE Transactions on Haptics最新文献

Current issues with neuromorphic visual-tactile perception include limited training network representation and inadequate cross-modal fusion. To address these two issues, we proposed a dual network called visual-tactile spiking graph neural network (VT-SGN) that combines graph neural networks and spiking neural networks to jointly utilize the neuromorphic visual and tactile source data. First, the neuromorphic visual-tactile data were expanded spatiotemporally to create a taxel-based tactile graph in the spatial domain, enabling the complete exploitation of the irregular spatial structure properties of tactile information. Subsequently, a method for converting images into graph structures was proposed, allowing the vision to be trained alongside graph neural networks and extracting graph-level features from the vision for fusion with tactile data. Finally, the data were expanded into the time domain using a spiking neural network to train the model and propagate it backwards. This framework effectively utilizes the structural differences between sample instances in the spatial dimension to improve the representational power of spiking neurons, while preserving the biodynamic mechanism of the spiking neural network. Additionally, it effectively solves the morphological variance between the two perceptions and further uses complementary data between visual and tactile. To demonstrate that our approach can improve the learning of neuromorphic perceptual information, we conducted comprehensive comparative experiments on three datasets to validate the benefits of the proposed VT-SGN framework by comparing it with state-of-the-art studies.

Numerous studies have indicated that the use of a closed-loop haptic feedback system, which offers various mechano-tactile stimuli patterns with different actuation methods, can improve the performance and grasp control of prosthetic hands. Purely mechanical-driven feedback approaches for various mechano-tactile stimuli patterns, however, have not been explored. In this paper, a multi-cavity fluidic haptic feedback system is introduced with details of design, fabrication, and validation. The multi-cavity haptic feedback system can detect the physical touch with direction at the fingertip sensor. The direction of the force is reflected in the form of pressure deviation in the multi-cavity fingertip sensor. The feedback actuator generates various mechano-tactile stimuli patterns according to the pressure deviation from the fingertip sensor. Hence, users can identify the force with direction according to the stimuli patterns. The haptic feedback system is validated through two experiments. The initial experiment characterises the system and establishes the relationship between the fingertip sensor and feedback actuator. The subsequent experiment, a human interaction test, confirms the system's capability to detect force with directions and generate corresponding tactile stimuli in the feedback actuator. The outcomes corroborate the idea that participants are generally capable of discerning changes in angle.

In cutaneous displays in which both tactile and thermal signals are presented, it is important to understand the temporal requirements associated with presenting these signals so that they are perceptually synchronous. Such synchrony is important to provide realistic touch experiences in applications involving object recognition and social touch interactions. In the present experiment the temporal window within which tactile and warm thermal stimuli are perceived to occur at the same time was determined. A Simultaneity Judgment Task was used in which pairs of tactile and thermal stimuli were presented on the hand at varying stimulus onset asynchronies, and participants determined whether the stimuli were simultaneous or not. The results indicated that the average simultaneity window width was 1041 ms. The average point of subjective simultaneity (PSS) was -569 ms, indicating that participants perceived simultaneity best when the warm thermal stimulus preceded the tactile stimulus by 569 ms. These findings indicate that thermal and tactile stimuli do not need to be displayed simultaneously for the two stimuli to be perceived as being synchronous and therefore the timing of such stimuli can be adjusted to maximize the likelihood that they will both be perceived.

Haptic rendering of weight plays an essential role in naturalistic object interaction in virtual environments. While kinesthetic devices have traditionally been used for this aim by applying forces on the limbs, tactile interfaces acting on the skin have recently offered potential solutions to enhance or substitute kinesthetic ones. Here, we aim to provide an in-depth overview and comparison of existing tactile weight rendering approaches. We categorized these approaches based on their type of stimulation into asymmetric vibration and skin stretch, further divided according to the working mechanism of the devices. Then, we compared these approaches using various criteria, including physical, mechanical, and perceptual characteristics of the reported devices. We found that asymmetric vibration devices have the smallest form factor, while skin stretch devices relying on the motion of flat surfaces, belts, or tactors present numerous mechanical and perceptual advantages for scenarios requiring more accurate weight rendering. Finally, we discussed the selection of the proposed categorization of devices together with the limitations and opportunities for future research. We hope this study guides the development and use of tactile interfaces to achieve a more naturalistic object interaction and manipulation in virtual environments.

We investigate the effect of finger moisture on the tactile perception of electroadhesion with 10 participants. Participants with moist fingers exhibited markedly higher threshold levels. Our electrical impedance measurements show a substantial reduction in impedance magnitude when sweat is present at the finger-touchscreen interface, indicating increased conductivity. Supporting this, our mechanical friction measurements show that the relative increase in electrostatic force due to electroadhesion is lower for a moist finger.

Humans rely on multimodal perception to form representations of the world. This implies that environmental stimuli must remain consistent and predictable throughout their journey to our sensory organs. When it comes to vision, electromagnetic waves are minimally affected when passing through air or glass treated for chromatic aberrations. Similar conclusions can be drawn for hearing and acoustic waves. However, tools that propagate elastic waves to our cutaneous afferents tend to color tactual perception due to parasitic mechanical attributes such as resonances and inertia. These issues are often overlooked, despite their critical importance for haptic devices that aim to faithfully render or record tactile interactions. Here, we investigate how to optimize this mechanical transmission with sandwich structures made from rigid, lightweight carbon fiber sheets arranged around a 3D-printed lattice core. Through a comprehensive parametric evaluation, we demonstrate how this design paradigm provides superior haptic transparency, regardless of the lattice types. Drawing an analogy with topology optimization, our solution approaches a foreseeable technological limit. It offers a practical way to create high-fidelity haptic interfaces, opening new avenues for research on tool-mediated interactions.

Wearable haptic devices provide touch feedback to users for applications including virtual reality, prosthetics, and navigation. When these devices are designed for experimental validation in research settings, they are often highly specialized and customized to the specific application being studied. As such, it can be difficult to replicate device hardware due to the associated high costs of customized components and the complexity of their design and construction. In this work, we present Snaptics, a simple and modular platform designed for rapid prototyping of fully wearable multi-sensory haptic devices using 3D-printed modules and inexpensive off-the-shelf components accessible to the average hobbyist. We demonstrate the versatility of the modular system and the salience of haptic cues produced by wearables constructed with Snaptics modules in two human subject experiments. First, we report on the identification accuracy of multi-sensory haptic cues delivered by a Snaptics device. Second, we compare the effectiveness of the Snaptics Vibrotactile Bracelet to the Syntacts Bracelet, a high-fidelity wearable vibration feedback bracelet, in assisting participants with a virtual reality sorting task. Results indicate that participant performance was comparable in perceiving cue sets and in completing tasks when interacting with low-cost Snaptics devices as compared to a similar research-grade haptic wearables.

Tactile feedback is essential for upper-limb prostheses functionality and embodiment, yet its practical implementation presents challenges. Users must adapt to non-physiological signals, increasing cognitive load. However, some prosthetic devices transmit tactile information through socket vibrations, even to untrained individuals. Our experiments validated this observation, demonstrating a user's surprising ability to identify contacted fingers with a purely passive, cosmetic hand. Further experiments with advanced soft articulated hands revealed decreased performance in tactile information relayed by socket vibrations as hand complexity increased. To understand the underlying mechanisms, we conducted numerical and mechanical vibration tests on four prostheses of varying complexity. Additionally, a machine-learning classifier identified the contacted finger based on measured socket signals. Quantitative results confirmed that rigid hands facilitated contact discrimination, achieving 83% accuracy in distinguishing index finger contacts from others. While human discrimination decreased with advanced hands, machine learning surpassed human performance. These findings suggest that rigid prostheses provide natural vibration transmission, potentially reducing the need for tactile feedback devices, which advanced hands may require. Nonetheless, the possibility of machine learning algorithms outperforming human discrimination indicates potential to enhance socket vibrations through active sensing and actuation, bridging the gap in vibration-transmitted tactile discrimination between rigid and advanced hands.

This study presents the characterization and validation of the VIBES, a wearable vibrotactile device that provides high-frequency tactile information embedded in a prosthetic socket. A psychophysical characterization involving ten able-bodied participants is performed to compute the Just Noticeable Difference (JND) related to the discrimination of vibrotactile cues delivered on the skin in two forearm positions, with the goal of optimising vibrotactile actuator position to maximise perceptual response. Furthermore, system performance is validated and tested both with ten able-bodied participants and one prosthesis user considering three tasks. More specifically, in the Active Texture Identification, Slippage and Fragile Object Experiments, we investigate if the VIBES could enhance users' roughness discrimination and manual usability and dexterity. Finally, we test the effect of the vibrotactile system on prosthetic embodiment in a Rubber Hand Illusion (RHI) task. Results show the system's effectiveness in conveying contact and texture cues, making it a potential tool to restore sensory feedback and enhance the embodiment in prosthetic users.

Virtual damping is often employed to improve stability in virtual environments, but it has previously been found to bias perception of stiffness, with its effects differing when it is introduced locally within a wall/object or globally in both the wall and in freespace. Since many potential applications of haptic rendering involve not only comparisons between two environments, but also the ability to recognize rendered environments as belonging to different categories, it is important to understand the perceptual impacts of freespace and wall damping on stiffness classification ability. This study explores the effects of varying levels of freespace and wall damping on users' ability to classify virtual walls by their stiffness. Results indicate that freespace damping improves wall classification if the walls are damped, but will impair classification of undamped walls. These findings suggest that, in situations where users are expected to recognize and classify various stiffnesses, freespace damping can be a factor in narrowing or widening gaps in extended rate-hardness between softer and stiffer walls.