IEEE Transactions on Haptics最新文献

Auditory and visual cues have been efficacious in laboratory-based freezing of gait (FoG) mitigation in Parkinson's disease (PD). However, real-life applications of these cues are restricted due to inconvenience to the users. Closed-loop vibrotactile cues based on temporal gait events have overcome the shortcomings of auditory and visual cueing. However, kinematic gait parameter-driven vibrotactile cueing has not been explored yet. Kinematic gait parameter-driven cueing is more effective than temporal cueing, according to FoG pathophysiology studies. Therefore, we developed and pilot-tested a novel cueing scheme in which the foot-to-ground angle at heel strike (FGA_HS) is estimated using indigenous instrumented shoes to drive vibrotactile cueing. Ten PD freezers underwent a 6-meter timed walk test in the off-medication state with and without the cue and after medication without the cue. The proposed system potentially mitigated FoG, quantified by a reduction in the ratio of time spent freezing to the total walking time and the number of FoGs. The FoG mitigation potential of the cue was further supported by increased anteroposterior center of pressure progression and FGA_HS. With a future comprehensive validation in a larger number of participants, the novel cue could likely be used in practice and commercialized.

Haptic devices are becoming popular in many applications, including medical, gaming, and consumer devices. Yet, the majority of studies focus on the use of haptics for the upper limbs, with much less attention to the stimulation of other regions of the body such as the lower back. In this study, we designed three types of skin stretch stimulation devices that can be placed on a belt and apply tactile stimulation on the lower back. We present these devices that apply lateral, longitudinal, and rotational skin stretch stimulation on the lower back, and evaluate their effectiveness in providing haptic commands for the lower limbs of healthy participants. We designed psychophysical experiments that quantify the discrimination accuracy of participants with a stepping task. The results demonstrate the ability of participants to discriminate two out of three features of stimulation provided on the lower back. These results demonstrate that skin stretch on the lower back can effectively transmit haptic signals and elicit responses in the lower limb for various applications. Future studies are needed to optimize providing skin stretch on the lower back to benefit various applications such as training, rehabilitation, gaming, and assistive devices.

This paper proposed linear and non-linear models for predicting human-exoskeleton coupling forces to enhance the studies of human-exoskeleton coupling dynamics. Then the parameters of these models were identified with a newly designed platform and the help of ten adult male and ten adult female volunteers (Age: 23.65 ±4.03 years, Height: 165.60 ±8.32 mm, Weight: 62.35 ±14.09 kg). Comparing the coupling force error predicted by the models with experimental measurements, one obtained a more accurate and robust prediction of the coupling forces with the non-linear model. Moreover, statistical analysis of the experimental data was performed to reveal the correlation between the coupling parameters and coupling positions and looseness. Finally, backpropagation (BP) neural network and Gaussian Process Regression (GPR) were used to predict the human-exoskeleton coupling parameters. The significance of each input parameter to the human-exoskeleton coupling parameters was assessed by analyzing the sensitivity of GPR performance to its inputs. The novelty and contribution are the establishment of the non-linear coupling model, the design of the coupling experimental platform and a regression model which provides a possibility to obtain human-exoskeleton without experimental measurement and identification. Based on this work, one can optimize control algorithm and design comfortable human-exoskeleton interaction.

This paper outlines the design, characterization, and validation of a novel wearable haptic device capable of delivering skin stretch, force feedback, or a combination of both, to the user's arm. In this study, we conducted physical and perceptual characterization with eleven able-bodied participants, and two separate experiments involving discrimination and manipulation tasks, encompassing a total of 32 participants. In both experiments, we used the CUFF device in conjunction with the Pisa/IIT SoftHand. The first experiment was a discrimination task in which participants were required to differentiate between pairs of cylinders based on their dimensions and perceived softness. The second experiment called for participants to control the robotic hand in order to grasp objects. Following the experiments, participants provided a subjective evaluation of the device. The results from the experiments and the participants' feedback underscored the effectiveness of the proposed device. Thanks to its versatility and structural design, the device shows promise as a viable solution for a variety of applications, including teleoperation, guidance, rehabilitation tasks, and prosthetic applications.

A method of providing localised haptic feedback at precise locations on the body, utilising a lightweight textile garment is presented in this short paper. The textile comprises of subtly integrated actuator yarns (HaptiYarns) which are controlled by electropneumatic circuitry. Each yarn has two functional layers, an inner porous textile layer with limited extensibility and a second, durable outer layer made from an extensible elastomer. The HaptiYarns can provide radial forces and a maximum radial displacement of 28.09 ± 0.14 mm. It was found that the intrinsic addition of graphite powder (5% by weight), during elastomer preparation, offered better resistance to layer delamination and increased the ability of the yarn to withstand higher internal air pressures by 48%. Both the graphite-filled composite and the graphite free yarns demonstrated high durability, withstanding cyclic testing of >7500 cycles while having no significant impact on the force feedback. Finally, a wearable prototype knitted textile garment is presented with eight HaptiYarns subtly integrated within it and connected to a virtual reality (VR) program providing an immersive haptic experience. These yarns offer the potential to transform everyday clothing into wearable haptic devices with potential to revolutionise healthcare, VR-based training, gaming, and entertainment sectors.

In this paper, we propose three methods to compute low-latency analog position where two of them fuse encoder and rate gyro signals. While one method is based on gyro with bias correction using encoder information, the other one is encoder-referenced combined with a resettable integrator to minimize the staircase form of encoder signals. Experiments on a one degree-of-freedom haptic simulation system have shown that a low-latency analog position with an accuracy over 98% compared to the sampled encoder signal can be obtained. The analog position signals are then utilized to produce analog viscoelastic virtual environments to assess and benchmark the proposed methods through uncoupled stability and perceived fidelity tests. The results have shown that a virtual stiffness range larger than 400% can be obtained with enhanced fidelity compared to common digital implementations.

The past decade has witnessed the development of tactile sensors, which have been increasingly considered as an essential equipment in robotics, especially the dexterous manipulation and collaborative human-robot interactions. There are two major types of tactile sensors, i.e., the vision-based and taxel-based sensors. The latter is capable of achieving lower integration complexity with existing robotic systems, but unable to provide high-resolution (HR) tactile information as that of the vision-based counterpart due to the manufacturing limitations. Therefore, we propose a novel tactile pattern super-resolution (SR) scheme for taxel-based sensors, which is a data-driven scheme enabling customized selection on the number of applied "tapping" actions to achieve improvable performance from single tapping SR (STSR) to the multi-tapping SR (MTSR). In addition, we develop a new dataset for the proposed tactile SR scheme. In order to obtain scalable resolutions (e.g. ×4, ×10, ×20, etc.) of ground-truth HR tactile patterns, we propose a novel tactile point spread function (PSF) scheme to generate HR tactile patterns by leveraging the low-resolution (LR) data gathered directly from the taxel-based sensor and the depth information of contact surfaces. This is in strong contrast to the conventional ground-truth generation approach with overlapped multi-sampling and registration strategy, which can only provide a fixed resolution. Experimental results confirm the efficiency of the proposed scheme.

Noisy vibrotactile signals transmitted during tactile explorations of an object provide precious information on the nature of its surface. Understanding the link between signal properties and how they are interpreted by the tactile sensory system remains challenging. In this paper, we investigated human perception of broadband, stationary vibrations recorded during exploration of textures and reproduced using a vibrotactile actuator. Since intensity is a well-established perceptual attribute, we here focused on the relevance of the spectral content. The stimuli were first equalized in perceived intensity and subsequently used to identify the most salient spectral features using dissimilarity estimations between pairs of successive vibration. Based on dimensionally reduced spectral representations, models of dissimilarity ratings showed that the balance between low and high frequencies was the most important cue. Formal validation of this result was achieved through a Mushra experiment, in which participants assessed the fidelity of resynthesized vibrations with various distorted frequency balances. These findings offer valuable insights into human vibrotactile perception and establish a computational framework for analyzing vibrations as humans do. Moreover, they pave the way for signal synthesis and compression based on sparse representations, holding significance for applications involving complex vibratory feedback.

This paper explores the design and experimental validation of a three-degree-of-freedom variable inertia generator. An inertia generator is a handheld haptic device that renders a prescribed inertia. In the mechanism proposed in this paper, three-dimensional torque feedback is achieved by accelerating three pairs of flywheels mounted on orthogonal axes. While the primary objective of this work is to design an inertia generator, this study also includes developing other functionalities for the device that exploit its torque generation capabilities. These include the ability to generate a predefined torque profile and to simulate a viscous environment through damping, which are both utilized to assess the device's performance. The device proved to accurately render the necessary torques for every functionality while presenting some limitations for damping and rendering an inertia smaller than the device's inherent inertia.