The interaction between motion and texture in the sense of touch

Crosstalk-Free Position Mapping for One-Step Reconstruction of Surface Topological Information via Eigenfrequency-Registered Wearable Interface

Exploring flexible tactile sensors capable of recognizing surface information is significant for the development of virtual reality, artificial intelligence, soft robotics, and human-machine interactions (HMI). However, it is still a challenge for current tactile sensors to efficiently recognize the surface pattern information while maintaining the simplicity of the overall system. In this study, cantilever beam-like magnetized micropillars (MMPs) with height gradients are assembled as a position-registered array for rapid recognition of surface pattern information. After crossing the surface location with convex patterns, the deformed MMPs undergo an intrinsic oscillating process to induce damped electrical signals, which can then be converted to a frequency domain for eigenfrequency extraction. Via precisely defining the specific eigenfrequencies of different MMPs, position mapping is realized in crosstalk-free behavior even though all signals are processed by one communication channel and a pair of electrodes. With a customized LabVIEW program, the surface information (e.g., letters, numbers, and Braille) can be accurately reconstructed by the frequency sequence produced in a single scanning procedure. We expect that the proposed interface can be a convenient and powerful platform for intelligent surface information perception and an HMI system in the future.

Effects of Temperature and Humidity on Energy Dissipation between Human Corneocytes and Nanoasperity Sliding Contacts

Human haptic perception relies on the ability of sensory receptors underneath the skin corneocyte layer to sense external load, where adhesion and friction play an essential role in nanoscale solid-solid contact. Energy dissipation present at the surface interface due to the change of separation distance during sliding contact was uncovered, but the energy dissipation of human finger skin cell-nanoprobe contact under humidity and temperature conditions has not been investigated yet. In this paper, the energy dissipation of skin corneocyte-nanoprobe interface under variation of both humidity, 0.05-80%RH, and temperature ranging from 25 to 40 °C is directly measured by atomic force microscopy (AFM). Analytical models of dissipation energy for this nanomaterial interface mechanism are developed, and the results are compared to the measured values. AFM measurements of dissipation energy reveal that the amount of dissipated energy caused by water meniscus stretching monotonically increases with humidity and temperature, resulting in adhesion and friction decreases. The purposed analytical model represents that dissipation energy trend.

Profiling Somatosensory Impairment after Stroke: Characterizing Common "Fingerprints" of Impairment Using Unsupervised Machine Learning-Based Cluster Analysis of Quantitative Measures of the Upper Limb

Altered somatosensory function is common among stroke survivors, yet is often poorly characterized. Methods of profiling somatosensation that illustrate the variability in impairment within and across different modalities remain limited. We aimed to characterize post-stroke somatosensation profiles ("fingerprints") of the upper limb using an unsupervised machine learning cluster analysis to capture hidden relationships between measures of touch, proprioception, and haptic object recognition. Raw data were pooled from six studies where multiple quantitative measures of upper limb somatosensation were collected from stroke survivors (n = 207) using the Tactile Discrimination Test (TDT), Wrist Position Sense Test (WPST) and functional Tactile Object Recognition Test (fTORT) on the contralesional and ipsilesional upper limbs. The Growing Self Organizing Map (GSOM) unsupervised machine learning algorithm was used to generate a topology-preserving two-dimensional mapping of the pooled data and then separate it into clusters. Signature profiles of somatosensory impairment across two modalities (TDT and WPST; n = 203) and three modalities (TDT, WPST, and fTORT; n = 141) were characterized for both hands. Distinct impairment subgroups were identified. The influence of background and clinical variables was also modelled. The study provided evidence of the utility of unsupervised cluster analysis that can profile stroke survivor signatures of somatosensory impairment, which may inform improved diagnosis and characterization of impairment patterns.

EEG-based functional connectivity for tactile roughness discrimination

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

A hierarchical sensorimotor control framework for human-in-the-loop robotic hands

Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands.

Effects of Vibration Direction and Pressing Force on Finger Vibrotactile Perception and Force Control

This paper reports about the effects of vibration direction and finger-pressing force on vibrotactile perception, with the goal of improving the effectiveness of haptic feedback on interactive surfaces. An experiment was conducted to assess the sensitivity to normal or tangential vibration at 250 Hz of a finger exerting constant pressing forces of 0.5 or 4.9 N. Results show that perception thresholds for normal vibration depend on the applied pressing force, significantly decreasing for the stronger force level. Conversely, perception thresholds for tangential vibrations are independent of the applied force, and approximately equal the lowest thresholds measured for normal vibration.

Bayesian hierarchical models and prior elicitation for fitting psychometric functions

Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods. The aim of this article is to revisit hierarchical models in a Bayesian framework. Bayesian models have been previously discussed for the analysis of psychometric functions although this approach is still seldom applied. The main advantage of using Bayesian models is that if the prior is informative, the uncertainty of the parameters is reduced through the combination of prior knowledge and the experimental data. Here, we evaluate uncertainties between and within participants through posterior distributions. To demonstrate the Bayesian approach, we re-analyzed data from two of our previous studies on the tactile discrimination of speed. We considered different methods to include a priori knowledge in the prior distribution, not only from the literature but also from previous experiments. A special type of Bayesian model, the power prior distribution, allowed us to modulate the weight of the prior, constructed from a first set of data, and use it to fit a second one. Bayesian models estimated the probability distributions of the parameters of interest that convey information about the effects of the experimental variables, their uncertainty, and the reliability of individual participants. We implemented these models using the software Just Another Gibbs Sampler (JAGS) that we interfaced with R with the package rjags. The Bayesian hierarchical model will provide a promising and powerful method for the analysis of psychometric functions in psychophysical experiments.

Masking Vibrations and Contact Force Affect the Discrimination of Slip Motion Speed in Touch

Multiple cues contribute to the discrimination of slip motion speed by touch. In our previous article, we demonstrated that masking vibrations at various frequencies impaired the discrimination of speed. In this article, we extended the previous results to evaluate this phenomenon on a smooth glass surface, and for different values of contact force and duration of the masking stimulus. Speed discrimination was significantly impaired by masking vibrations at high but not at low contact force. Furthermore, a short pulse of masking vibrations at motion onset produced a similar effect as the long masking stimulus, delivered throughout slip motion duration. This last result suggests that mechanical events at motion onset provide important cues to the discrimination of speed.

Hand posture, but not vision of the hand, affects tactile spatial resolution in the grating orientation discrimination task

The grating orientation discrimination task (GOT) is a sensitive and reliable measure of tactile spatial resolution, or acuity. We used the GOT in three experiments to investigate the effects of hand posture and hand visibility on spatial acuity. Participant sex and experimental design were also manipulated. Healthy adult participants received brief touches to their index fingertips of grated, domed objects. Their task was to decide whether the gratings ran 'along' or 'across' their finger. Measures of the smallest grating width for which participants could reliably discriminate between orientations were recorded as threshold. Experiment 1 evaluated the effect of two- versus one-interval discrimination, hand used and participant sex. Experiments 2 and 3 evaluated the effects of hand visibility (visible or covered) and hand posture (in front or to the side). Females were better than males; the two-interval task resulted in lower thresholds than the one-interval task; and left and right hand thresholds were not significantly different. Most importantly, while hand visibility did not have a significant effect on the task, thresholds were affected by hand posture—worse when the hand was oriented to the side of the body than in front. These results replicate previously reported effects of sex (or finger size), but failed to replicate the so-called ‘visual enhancement of touch’ (VET) effect. We also report a meta-analysis of 27 VET studies, finding a significant effect of ‘non-informative’ vision on tactile perception. Our novel finding is that hand posture affects tactile acuity.

Enhancing touch sensibility by sensory retraining in a sensory discrimination task via haptic rendering

Stroke survivors are commonly affected by somatosensory impairment, hampering their ability to interpret somatosensory information. Somatosensory information has been shown to critically support movement execution in healthy individuals and stroke survivors. Despite the detrimental effect of somatosensory impairments on performing activities of daily living, somatosensory training—in stark contrast to motor training—does not represent standard care in neurorehabilitation. Reasons for the neglected somatosensory treatment are the lack of high-quality research demonstrating the benefits of somatosensory interventions on stroke recovery, the unavailability of reliable quantitative assessments of sensorimotor deficits, and the labor-intensive nature of somatosensory training that relies on therapists guiding the hands of patients with motor impairments. To address this clinical need, we developed a virtual reality-based robotic texture discrimination task to assess and train touch sensibility. Our system incorporates the possibility to robotically guide the participants' hands during texture exploration (i.e., passive touch) and no-guided free texture exploration (i.e., active touch). We ran a 3-day experiment with thirty-six healthy participants who were asked to discriminate the odd texture among three visually identical textures –haptically rendered with the robotic device– following the method of constant stimuli. All participants trained with the passive and active conditions in randomized order on different days. We investigated the reliability of our system using the Intraclass Correlation Coefficient (ICC). We also evaluated the enhancement of participants' touch sensibility via somatosensory retraining and compared whether this enhancement differed between training with active vs. passive conditions. Our results showed that participants significantly improved their task performance after training. Moreover, we found that training effects were not significantly different between active and passive conditions, yet, passive exploration seemed to increase participants' perceived competence. The reliability of our system ranged from poor (in active condition) to moderate and good (in passive condition), probably due to the dependence of the ICC on the between-subject variability, which in a healthy population is usually small. Together, our virtual reality-based robotic haptic system may be a key asset for evaluating and retraining sensory loss with minimal supervision, especially for brain-injured patients who require guidance to move their hands.

The evaluation of tactile dysfunction in the hand in type 1 diabetes: a novel method based on haptics

AIMS

We present an innovative method based on haptics for the evaluation of the sense of touch in the hand, in people affected by type 1 diabetes.

METHODS

Forty individuals affected by diabetes and 20 healthy controls took part in the study; the diabetes group was further divided into two subgroups based on vibration sensitivity in the lower limb. By means of a novel haptic device, tactile sensitivity in the fingertip was measured as the ability of the participants to discriminate slip motion speed.

RESULTS

Tactile sensitivity was significantly lower in individuals affected by diabetes as compared to controls. Depending on the subgroup, the difference from the controls was equal to 0.11 (95% CI from 0.029 to 0.186) and to 0.267 (95% CI from 0.198 to 0.336). Within the diabetes group, tactile sensitivity correlated with vibration sensitivity in the upper (p = 0.001) and lower limb (p = 0.003). A significant relationship between nerve conduction parameters and tactile sensitivity was found (p = 0.03). Finally, we combined the different predictors (clinical, vibratory and electroneurography data) by using cluster analysis; tactile sensitivity was found to be significantly different between different clusters (p = 0.004).

CONCLUSIONS

Early signs of tactile dysfunction in the hand were found in individuals affected by diabetes, even in absence of diabetic neuropathy. The protocol presented in this study is a promising tool for the assessment of tactile dysfunction in the hand in people affected by type 1 diabetes.

Predictive attenuation of touch and tactile gating are distinct perceptual phenomena

In recent decades, research on somatosensory perception has led to two important observations. First, self-generated touches that are predicted by voluntary movements become attenuated compared with externally generated touches of the same intensity (attenuation). Second, externally generated touches feel weaker and are more difficult to detect during movement than at rest (gating). At present, researchers often consider gating and attenuation the same suppression process; however, this assumption is unwarranted because, despite more than 40 years of research, no study has combined them in a single paradigm. We quantified how people perceive self-generated and externally generated touches during movement and rest. We show that whereas voluntary movement gates the precision of both self-generated and externally generated touch, the amplitude of self-generated touch is robustly attenuated compared with externally generated touch. Furthermore, attenuation and gating do not interact and are not correlated, and we conclude that they represent distinct perceptual phenomena.

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We tested the perception of self-generated and external touch during movement and rest

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The intensity of self-generated touch is reduced during movement and rest (attenuation)

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The precision of self-generated and external touch is reduced during movement (gating)

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Attenuation and gating neither interact nor correlate, and are distinct phenomena