Optimizing a Viscoelastic Finite Element Model to Represent the Dry, Natural, and Moist Human Finger Pressing on Glass

Abstract

When a fingerpad presses into a hard surface, the development of the contact area depends on the pressing force and speed. Importantly, it also varies with the finger's moisture, presumably because hydration changes the tissue's material properties. Therefore, we collected data from one finger repeatedly pressing a glass plate under three moisture conditions, and we constructed a finite element model that we optimized to simulate the same three scenarios. We controlled the moisture of the subject's finger to be dry, natural, or moist and recorded 15 pressing trials in each condition. The measurements include normal force over time plus finger-contact images that are processed to yield gross contact area. We defined the axially symmetric 3D model's lumped parameters to include an SLS-Kelvin model (spring in series with parallel spring and damper) for the bulk tissue, plus an elastic epidermal layer. Particle swarm optimization was used to find the parameter values that cause the simulation to best match the trials recorded in each moisture condition. The results show that the softness of the bulk tissue reduces as the finger becomes more hydrated. The epidermis of the moist finger model is softest, while the natural finger model has the highest viscosity.

Contact evolution of dry and hydrated fingertips at initial touch

“…In addition, these two variables are both influenced by moisture, an important external factor. An increase in the fingertip moisture level softens the finger [10][11][12] while also changing the skin-surface friction coefficient [13][14][15]. The fingerprint ridges further complicate touch mechanics, as they cause non-uniform contact area as well as additional effects on the elastic behavior [4,16] and friction [17,18] when moisture is involved.…”

The effect of contact pressure on photoplethysmography

“…Figure 4 describes the axially symmetric 3D finger model. The dimension parameters come from the literature [16]- [20]. This model proves a lower computational load than the non-axisymmetric model, enabling the repetition of the simulations with varying parameter combinations [16].…”

“…The dimension parameters come from the literature [16]- [20]. This model proves a lower computational load than the non-axisymmetric model, enabling the repetition of the simulations with varying parameter combinations [16]. The optical sensor is abstracted as a glass plate with a ISSN: 2502-4752 …”

“…We search for the optimal parameter values, with the objective of finding the parameter lump that enables the lowest sum of MSE across the 15 measurement experiments, to cause the model's simulated contact area to evolve in the same way as the contact area measured. Possible values were chosen by referring to the research on Young's modulus of each layer [22], which was reported as: 136 kPa for the epidermis, 80 kPa for the dermis, and 34 kPa for the subcutaneous tissue, and the simulation results of Nam and Kuchenbecker [16]. Then an artificial search for the values to maximize the objection function was conducted.…”

“…During the process of deformation under external pressure, there are two primary changes in the skin: the compression of skin thickness and the increase in optical coefficient. The material of human skin demonstrates significant nonlinearity and anisotropy, the parameters of linear and nonlinear models to characterize the material properties of skin would be influenced by several factors such as age, race, gender, emotions, and environmental circumstances, the values can exhibit significant variations over 4 to 5 orders (4.4 kPa-57 MPa) [16]. Hence, before reproducing fingertip pressing behavior by the model, it is imperative to ascertain the skin material properties of the subject under the current experimental conditions.…”