Deformation Of Finger Pad Research Articles

Collagen Induces Anisotropy in Fingertip Subcutaneous Tissues During Contact.

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 sectionof 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.

Multiple Spatial Spectral Components of Static Skin Deformation for Predicting Macroscopic Roughness Perception.

A previous study suggested a relationship between the spatial spectrum of finger pad skin deformation and perception of macroscopic roughness features. This study tested a new hypothesis that macroscopic roughness perception is the result of a weighted linear combination of multiple spatial spectral components of skin deformation. Experiments were conducted by capturing close-up images of finger pad deformation while the pads were pushed onto specimens with macroscopic features. Additionally, the roughness perceptions of these specimens were collected using a magnitude estimation method. The combination of spectral components predicted the roughness perception more accurately than any single spectral component. This suggests that roughness perception is mediated by multiple Gabor filter-like neural systems with different spatial periods, such as visual perception.

Analysis of Finger Pad Deformation by 3D Measurement

Analysis of Finger Pad Deformation by 3D Measurement

Measuring fingerpad deformation during active object manipulation.

During active object manipulation, the finger-object interactions give rise to complex fingertip skin deformations. These deformations are in turn encoded by the local tactile afferents and provide rich and behaviorally relevant information to the central nervous system. Most of the work studying the mechanical response of the finger to dynamic loading has been performed under a passive setup, thereby precisely controlling the kinematics or the dynamics of the loading. However, to identify aspects of the deformations that are relevant to online control during object manipulation, it is desirable to measure the skin response in an active setup. To that end, we developed a device that allows us to monitor finger forces, skin deformations, and kinematics during fine manipulation. We describe the device in detail and test it to precisely describe how the fingertip skin in contact with the object deforms during a simple vertical oscillation task. We show that the level of grip force directly influences the fingerpad skin strains and that the strain rates are substantial during active manipulation (norm up to 100%/s). The developed setup will enable us to causally relate sensory information, i.e. skin deformation, to online control, i.e. grip force adjustment, in future studies.NEW & NOTEWORTHY We present a novel device, a manipulandum, that enables to image the contact between the finger and the contact surface during active manipulation of the device. The device is tested in a simple vertical oscillation task involving 18 participants. We demonstrate that substantial surface skin strains take place at the finger-object interface and argue that those deformations provide essential information for grasp stability during object manipulation.

Tactile Perception for Teleoperated Robotic Exploration within Granular Media

The sense of touch is essential for locating buried objects when vision-based approaches are limited. We present an approach for tactile perception when sensorized robot fingertips are used to directly interact with granular media particles in teleoperated systems. We evaluate the effects of linear and nonlinear classifier model architectures and three tactile sensor modalities (vibration, internal fluid pressure, fingerpad deformation) on the accuracy of estimates of fingertip contact state. We propose an architecture called the Sparse-Fusion Recurrent Neural Network (SF-RNN) in which sparse features are autonomously extracted prior to fusing multimodal tactile data in a fully connected RNN input layer. The multimodal SF-RNN model achieved 98.7% test accuracy and was robust to modest variations in granular media type and particle size, fingertip orientation, fingertip speed, and object location. Fingerpad deformation was the most informative modality for haptic exploration within granular media while vibration and internal fluid pressure provided additional information with appropriate signal processing. We introduce a real-time visualization of tactile percepts for remote exploration by constructing a belief map that combines probabilistic contact state estimates and fingertip location. The belief map visualizes the probability of an object being buried in the search region and could be used for planning.

Measurement of Finger Pad Deformation during Press Motion

Measurement of Finger Pad Deformation during Press Motion

Perception of Tactile Directionality via Artificial Fingerpad Deformation and Convolutional Neural Networks.

Humans can perceive tactile directionality with angular perception thresholds of 14-40° via fingerpad skin displacement. Using deformable, artificial tactile sensors, the ability to perceive tactile directionality was developed for a robotic system to aid in object manipulation tasks. Two convolutional neural networks (CNNs) were trained on tactile images created from fingerpad deformation measurements during perturbations to a handheld object. A primary CNN regression model provided a point estimate of tactile directionality over a range of grip forces, perturbation angles, and perturbation speeds. A secondary CNN model provided a variance estimate that was used to determine uncertainty about the point estimate. A 5-fold cross-validation was performed to evaluate model performance. The primary CNN produced tactile directionality point estimates with an error rate of 4.3% for a 20° angular resolution and was benchmarked against an open-source force estimation network. The model was implemented in real-time for interactions with an external agent and the environment with different object shapes and widths. The perception of tactile directionality could be used to enhance the situational awareness of human operators of telerobotic systems and to develop decision-making algorithms for context-appropriate responses by semi-autonomous robots.

A Wearable Three-Axis Tactile Sensor for Human Fingertips

Human training data can scaffold robot imitation learning. However, most datagloves only record joint angles, but for many tasks, force control is more important. They also cover the human skin, thereby making a natural interaction of the human with the object impossible. This letter suggests a wearable sensor that can measure the three-axis force vector exerted at the fingertip, without covering the skin that contacts the object. Using two small-sized Hall effect based three-axis sensors mounted on the sides of the fingertips, the finger pad deformation resulting from the force vector acting on the fingertip is measured. Experiments with ten subjects show that the force vectors can be measured with reasonable precision. Furthermore, the influence of the Earth's magnetic field and the finger's orientation on the finger tactile sensor's measurement has been reduced from $\pm 4N$ to $\pm 1N$ .

Wearable finger pad deformation sensor for tactile textures in frequency domain by using accelerometer on finger side

In this study, we set out to develop a method for estimating the fine and fast shear deformation of a finger pad, that is, the palm side of a fingertip, as it scans the surface of a material. Using a miniature accelerometer, we measured the acceleration at the radial skin, the deformation of which is accompanied by the shear deformation of the finger pad. Using a transfer function, as specified in a separate experiment, between the pad and side of a finger, we estimated the shear deformation of the finger pad in the frequency domain. A comparison between an estimate based on the accelerometer and another based on a precise force sensor for the tangential component of the interaction force between the fingertip and material surfaces showed that the estimation accuracy was sufficient for frequencies in excess of approximately 20–50 Hz and for skin deformation above 10^{-6} m. Our technique merely requires that an accelerometer be attached to the side of the fingertip, which allows active texture exploration. These estimates or measurements of the finger skin deformation caused by touching materials will help us to comprehend the relationships between material surfaces and the resulting texture sensations.

New Non-invasive Techniques to Quantify Skin Surface Strain and Sub-surface Layer Deformation of Finger-pad during Sliding

Studies on the variation of skin properties with gender, age and anatomical region, with regards to interaction with different materials have resulted in significant research output. Investigations on skin surface strain and sub-surface layer deformation during sliding, however, have not received as much attention. This novel study uses two non-invasive techniques, optical coherence tomography (OCT) and digital image correlation (DIC), to measure properties of the index finger of a 25year old female when under normal and shear loading. Measurements were taken during static, and for the first time, dynamic phases.It was observed that the number of ridges in contact with a Quartz glass surface, observed under OCT, reduced when the finger started sliding. The sliding also resulted in deformation at the stratum corneum junction. The surface strain, analysed using DIC was higher nearer to the distal interphalangeal joint compared to the fingertip. This newly developed approach provides a powerful and non-invasive method to study the structural changes of finger-pad skin during loading and sliding. This approach can now be repeated at different anatomical locations for medical, bioengineering and consumer applications.

Force-Rate Cues Reduce Object Deformation Necessary to Discriminate Compliances Harder than the Skin.

Grasping and manipulating an object requires us to perceive its material compliance. Compliance is thought to be encoded by relationships of force, displacement, and contact area at the finger pad. Prior work suggests that objects must be sufficiently deformed to become discriminable, but the utility of time-dependent cues has not been fully explored. The studies herein find that the availability of force-rate cues improve compliance discriminability so as to require less deformation of stimulus and finger pad. In particular, we tested the impact of controlling force-rate and displacement-rate cues in passive touch psychophysical experiments. An ink-based method to mark the finger pad was used to measure contact area per stimulus, simultaneously with displacement and force. Compliances spanned a range harder and softer than the finger pad. The results indicated harder compliances were discriminable at lower peak forces when the stimulus control mode was displacement-rate (0.5 N) compared to force-rate (1.3 N). That is, when displacement-rate was controlled to be equal between the two compliances, the resultant force-rate psychophysical cues could be more readily discriminated. In extending prior studies, while some magnitude of finger pad deformation may be sufficient for discriminability, temporal cues tied to force afford more efficient judgments.

Multisensory components of rapid motor responses to fingertip loading.

Tactile and muscle afferents provide critical sensory information for grasp control, yet the contribution of each sensory system during online control has not been clearly identified. More precisely, it is unknown how these two sensory systems participate in online control of digit forces following perturbations to held objects. To address this issue, we investigated motor responses in the context of fingertip loading, which parallels the impact of perturbations to held objects on finger motion and fingerpad deformation, and characterized surface recordings of intrinsic (first dorsal interosseous, FDI) and extrinsic (flexor digitorum superficialis, FDS) hand muscles based on statistical modeling. We designed a series of experiments probing the effects of peripheral stimulation with or without anesthesia of the finger, and of task instructions. Loading of the fingertip generated a motor response in FDI at ~60 ms following the perturbation onset, which was only driven by muscle stretch, as the ring-block anesthesia reduced the gain of the response occurring later than 90 ms, leaving responses occurring before this time unaffected. In contrast, the motor response in FDS was independent of the lateral motion of the finger. This response started at ~90 ms on average and was immediately adjusted to task demands. Altogether these results highlight how a rapid integration of partially distinct sensorimotor circuits supports rapid motor responses to fingertip loading.NEW & NOTEWORTHY To grasp and manipulate objects, the brain uses touch signals related to skin deformation as well as sensory information about motion of the fingers encoded in muscle spindles. Here we investigated how these two sensory systems contribute to feedback responses to perturbation applied to the fingertip. We found distinct response components, suggesting that each sensory system engages separate sensorimotor circuits with distinct functions and latencies.

2A2-X07 指腹側部の変形から指腹変形を推定する技術 : 力センサより推定された指腹変形との比較による妥当性の検証

2A2-X07 指腹側部の変形から指腹変形を推定する技術 : 力センサより推定された指腹変形との比較による妥当性の検証

Amplifying shear deformation of finger pad increases tracing distances

Human sensory inputs and motor outputs mutually affect one another. We pursue the idea that a tactile interface can influence human motor outputs by intervening in sensory–motor relationships. This study focuses on the shear deformation of a finger pad while a person traces a line or circle. During these tracing movements, the finger pads were deformed using a tactile interface. The tracing distances increased when the finger pad deformations were amplified by the tactile interface, which indicates that the intervention in the haptic sensorimotor loop affected the tracing movements. Elucidation of such interaction between the tracing movements and the shear deformations of finger pads enhances the understanding of human-assistive haptic techniques.

2A1-A11 TAKO-Pen : 皮膚への吸引圧刺激を用いたペン型疑似力覚インタフェース 第2報:ペン把持部への外力負荷時の指腹部変形解析(触覚と力覚(1))

We have developed the TAKO-Pen, a pen-type pseudo-haptic interface using suction pressure stimulation on the skin. This paper presents a follow up report to support the results obtained in an earlier study. In this paper, we obtain an analysis on the finger pad deformation using the finite element method. The analysis was based on the grasping condition of the finger on the pen at which external force is applied. This analysis is important for developing the guidelines for controlling pressure distribution on the skin. We found that strain energy density appears to conform to the responses we had obtained in the psychophysical experiment.

Experiment of Finger Deformation by Pneumatic Nozzle

In order to study the relation between tactile sense and finger pad deformation during a tactile exploration task, a non-contact technique to measure the finger pad deformation was proposed in this paper. The finger pad deformation can be measured with high temporal and spatial resolution via laser displacement sensor installed on an XY movement workbench. The experimental results of the finger pad deformation in different conditions show that the characteristic of finger skin is viscoelastic. The parameters of viscoelastic model can be calculated by fitting method using the Kelvin model. They are helpful for modeling the mechanoreceptor responses in skin to improve the understanding of the mechanical behavior of the finger pad and of the relation between mechanical stimuli and tactile perception.