Analysis of Hand Contact Areas and Interaction Capabilities During Manipulation and Exploration

Gonzalez, Franck; Gosselin, Florian; Bachta, Wael

doi:10.1109/toh.2014.2321395

Cited by 42 publications

(19 citation statements)

References 22 publications

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“…We treated the tip of the index finger as representative of the whole hand and measured and analyzed its position. We choose the index finger because it is known to be most often used for manual exploration, and the rest of the hand is less involved 28 . Still, the previous study focused on the judgment of basic tactile properties such as roughness and hardness, and little is known about how humans move their hands and fingers to judge tactile preference.…”

Section: Discussionmentioning

confidence: 99%

Frequency of Switching Touching Mode Reflects Tactile Preference Judgment

Yokosaka

Inubushi

Kuroki

et al. 2020

Sci Rep

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with participants' hand motion regardless of the task, while the correlation for some other features (e.g., warmth) depended on it. It can be speculated that for some perceptual features, the bottom-up effect is dominant in hand motion, while for others, the top-down effect is. Since preference, the main topic of this study, may reflect combinations of multiple perceptual aspects, it is highly likely that hand motion to judge preference will change as a result of both effects. We hypothesized that studying the strategy used (i.e., how an object is explored) to judge preference for an object's surface will reveal how humans extract information about tactile preference. An earlier study, by showing that the averaged velocity of finger motion did not have a correlation with pleasantness ratings, suggested that there is no bottom-up effect on hand motion 4 . However, that study restricted participants' touching mode to stroking only; therefore, the effect of the explorative strategy, i.e., the top-down effect, on preference judgment remains unknown.Capturing the features of the touching mode from complex patterns of hand motion is challenging, since the touching mode category is not explicit in most cases and participants may use more than one mode. It is known that how participants touch an object is time-varying even within a single trial, where the tactile stimulus is not changed and physical properties are constant. For example, participants sequentially have used a couple of touching modes, such as grasping and stroking, to judge an object's properties [13][14][15][16] . This view is also supported by a constructive approach showing that a robot arm could identify a tactile stimulus with higher accuracy by sequentially using multiple touching modes 17,18 . Other studies have shown that the averaged velocity and force tends to increase within a single trial 8,19 . Clearly, we need a better index than a categorical name or temporally averaged value to capture the temporal dynamics of hand motion.Here, we introduce nonlinear time series analysis as an effective tool for understanding the temporal dynamics of explorative hand motion, and try to reveal the links between the observed temporal dynamics of unrestricted hand motion and preference rating. Nonlinear time series analysis is a well-established method often used for analyses of other kinds of explorative motion such as eye movement during scans of radiographs 20 and wielding-hand motion during judgment of rod length 21 . One of the useful tools in the nonlinear time series analysis is the recurrence plot (RP) technique, which provides insight into the dynamics graphically 22 . Moreover, by calculating some characteristic values from RPs, such as DET, Lmax, and TREND as described below, we can quantitatively study the randomness, predictability, and stationarity of the time series, which is referred to as recurrence quantification analysis. Our main finding is that the index of stationarity (TREND) had a positive correlation with preference rating. We also fou...

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Section: Discussionmentioning

confidence: 99%

Frequency of Switching Touching Mode Reflects Tactile Preference Judgment

Yokosaka

Inubushi

Kuroki

et al. 2020

Sci Rep

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“…Hand manipulation is heavily relied on intuitive human-object interaction and environment exploration. Primary hand-object contacts happen on fingertips and are perceived by mechanoreceptors during hand manipulation [1]. Mechanoreceptors are sensory units which distributed in the human skin to detect mechanical stimulations such as the force, vibration and pressure.…”

Section: Introductionmentioning

confidence: 99%

Haptics model for human fingertips based on gaussian distribution

Fang

Zhou

et al. 2019

IFS

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Functionality and cosmetics are two concerns for future hand prosthesis development and they both can be improved by a combination with artificial soft materials which can mimic human skin. To bridge the gap between the human and artificial side, it is essential to have a comprehensive understanding of the human skin's biomechanics, especially the fingertip's hapticsrelated mechanism. Available studies characterise the mechanical behaviour of human fingertip only by deterministic models based on either statistical data analysis or fingertip structure/viscoelasticity analysis. To take the force uncertainty into consideration, this paper proposes a novel probability-based haptics model, which includes two parts: a force prediction model to obtain the most possible contact force according to the indentation depth, and a probabilistic model based on Gaussian distribution to describe the force uncertainty. Experiments were conducted by pressing subjects' index fingertips against a cone-shape probe with the measurement of the contact force and the indentation depth under a wide range of 0~5 mm. Four types of non-linear regression models and the Gaussian distribution model are applied for model training and validation. Experiment results reveal that the contact force varying with the indentation depth presents the characteristics of non-linearity, dispersion, and individual difference. Model testing results confirm the effectiveness of the haptics model on force prediction and force uncertainty description. An example of its application on a virtual hand of a rehabilitation system is demonstrated.

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“…The boundary conditions of dexterous manipulations correspond to compressions up to 10 N, with various loading angles (Gonzalez et al, 2014;Daams, 1994). Prior experiments (Table 1) were carried out with loadings from 1 to 6N (Serina et al, 1997;Pawluk and Howe, 1999;Shimawaki and Sakai, 2007;Pérez-González et al, 2013) or with a prescribed 2mm-deflection (Srinivasan, 1989;Wu et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

MRI-based experimentations of fingertip flat compression: Geometrical measurements and finite element inverse simulations to investigate material property parameters

Dallard

Merlhiot

Petitjean

et al. 2018

Journal of Biomechanics

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Modeling human-object interactions is a necessary step in the ergonomic assessment of products. Fingertip finite element models can help investigating these interactions, if they are built based on realistic geometrical data and material properties. The aim of this study was to investigate the fingertip geometry and its mechanical response under compression, and to identify the parameters of a hyperelastic material property associated to the fingertip soft tissues. Fingertip compression tests in an MRI device were performed on 5 subjects at either 2 or 4 N and at 15° or 50°. The MRI images allowed to document both the internal and external fingertip dimensions and to build 5 subject-specific finite element models. Simulations reproducing the fingertip compression tests were run to obtain the material property parameters of the soft tissues. Results indicated that two ellipses in the sagittal and longitudinal plane could describe the external fingertip geometry. The internal geometries indicated an averaged maximal thickness of soft tissues of 6.4 ± 0.8 mm and a 4 ± 1 mm height for the phalanx bone. The averaged deflections under loading went from 1.8 ± 0.3 mm at 2 N, 50° to 3.1 ± 0.2 mm at 4 N, 15°. Finally, the following set of parameters for a second order hyperelastic law to model the fingertip soft tissues was proposed: C=0.59 ± 0.09 kPa and C = 2.65 ± 0.88 kPa. These data should facilitate further efforts on fingertip finite element modeling.

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Analysis of Hand Contact Areas and Interaction Capabilities During Manipulation and Exploration

Cited by 42 publications

References 22 publications

Frequency of Switching Touching Mode Reflects Tactile Preference Judgment

Frequency of Switching Touching Mode Reflects Tactile Preference Judgment

Haptics model for human fingertips based on gaussian distribution

MRI-based experimentations of fingertip flat compression: Geometrical measurements and finite element inverse simulations to investigate material property parameters

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