Justification for a 2D versus 3D fingertip finite element model during static contact simulations

Analysis of subject specific grasping patterns

Existing haptic feedback devices are limited in their capabilities and are often cumbersome and heavy. In addition, these devices are generic and do not adapt to the users’ grasping behavior. Potentially, a human-oriented design process could generate an improved design. While current research done on human grasping was aimed at finding common properties within the research population, we investigated the dynamic patterns that make human grasping behavior distinct rather than generalized, i.e. subject specific. Experiments were conducted on 31 subjects who performed grasping tasks on five different objects. The kinematics and kinetics parameters were measured using a motion capture system and force sensors. The collected data was processed through a pipeline of dimensionality reduction and clustering algorithms. Using finger joint angles and reaction forces as our features, we were able to classify these tasks with over 95% success. In addition, we examined the effects of the objects’ mechanical properties on those patterns and the significance of the different features for the differentiation. Our results suggest that grasping patterns are, indeed, subject-specific; this, in turn, could suggest that a device capable of providing personalized feedback can improve the user experience and, in turn, increase the usability in different applications. This paper explores an undiscussed aspect of human dynamic patterns. Furthermore, the collected data offer a valuable dataset of human grasping behavior, containing 1083 grasp instances with both kinetics and kinematics data.

SIMPLIFIED VERSUS REAL GEOMETRY FINGERTIP MODELS: A FINITE ELEMENT STUDY TO PREDICT FORCE–DISPLACEMENT RESPONSE UNDER FLAT CONTACT COMPRESSION

Finite element fingertip models are useful tools to assess product ergonomics. While “real geometry” approaches provide accurate results, developing models requires medical images. “Simpified geometry” approaches have to date not been tested to see whether they can provide equally accurate results in terms of mechanical response, i.e. force-displacement response and dimensions of fingertip contact area. Four fingertip models were built either from medical images (Visible Human project) or from simplified geometries. Simulations of fingertip flat contact compression at 20[Formula: see text] were performed. A 2nd order hyperelastic material property was used to effectively reproduce the mechanical behavior of the fingertip. Models based on simplified geometries such as conics proved as accurate as models reconstructed from medical images. However, accurate positioning of the bony phalanx is paramount if a biofidelic mechanical response is to be reproduced.

3D MRI Modeling of Thin and Spatially Complex Soft Tissue Structures without Shrinkage: Lamprey Myosepta as an Example

3D imaging techniques enable the nondestructive analysis and modeling of complex structures. Among these, MRI exhibits good soft tissue contrast, but is currently less commonly used for nonclinical research than X-ray CT, even though the latter requires contrast-staining that shrinks and distorts soft tissues. When the objective is the creation of a realistic and complete 3D model of soft tissue structures, MRI data are more demanding to acquire and visualize and require extensive post-processing because they comprise noncubic voxels with dimensions that represent a trade-off between tissue contrast and image resolution. Therefore, thin soft tissue structures with complex spatial configurations are not always visible in a single MRI dataset, so that standard segmentation techniques are not sufficient for their complete visualization. By using the example of the thin and spatially complex connective tissue myosepta in lampreys, we developed a workflow protocol for the selection of the appropriate parameters for the acquisition of MRI data and for the visualization and 3D modeling of soft tissue structures. This protocol includes a novel recursive segmentation technique for supplementing missing data in one dataset with data from another dataset to produce realistic and complete 3D models. Such 3D models are needed for the modeling of dynamic processes, such as the biomechanics of fish locomotion. However, our methodology is applicable to the visualization of any thin soft tissue structures with complex spatial configurations, such as fasciae, aponeuroses, and small blood vessels and nerves, for clinical research and the further exploration of tensegrity. Anat Rec, 301:1745-1763, 2018. © 2018 Wiley Periodicals, Inc.

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

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: C₀₁=0.59 ± 0.09 kPa and C₂₀ = 2.65 ± 0.88 kPa. These data should facilitate further efforts on fingertip finite element modeling.

Effect of 3D microstructure of dermal papillae on SED concentration at a mechanoreceptor location

The feeling of touch is an essential human sensation. Four types of mechanoreceptors (i.e., FA-I, SA-I, FA-II, and SA-II) in human skin signalize physical properties, such as shape, size, and texture, of an object that is touched and transmit the signal to the brain. Previous studies attempted to investigate the mechanical properties of skin microstructure and their effect on mechanoreceptors by using finite element modeling. However, very few studies have focused on the three-dimensional microstructure of dermal papillae, and this is related to that of FA-I receptors. A gap exists between conventional 2D models of dermal papillae and the natural configuration, which corresponds to a complex and uneven structure with depth. In this study, the three-dimensional microstructure of dermal papillae is modeled, and the differences between two-dimensional and three-dimensional aspects of dermal papillae on the strain energy density at receptor positions are examined. The three-dimensional microstructure has a focalizing effect and a localizing effect. Results also reveal the potential usefulness of these effects for tactile sensor design, and this may improve edge discrimination.