Innovative Computational Models

High-fidelity Human Fingertip Models

The sensation of touch originates from the skin deformation, which in turn stimulates the mechanoreceptors that send neural signals to the brain. I am developing detailed computational models for investigating the deformation mechanics of the fingertip, which is one of the most sensitive areas of the human body. I aim to build simulation tools that will enable a deeper understanding of physical phenomena occuring within the soft tissue and skin-surface interface. Such numerical means can also be used for circumventing a portion of costly experimental procedures in the design of intelligent systems with the aim of delivering various realistic haptic experiences.

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Calibrating Tactile Sensors with Simulated Data

Neural networks can also be trained with the simulated data! In this collaborative work, we use sim-to-real transfer learning for calibrating a soft tactile sensor electrical resistance tomography. with a Multiphysics Model and . We optimized the model parameters to reduce the gap between the simulation and reality. calibration is challenging because pressure reconstruction is an ill-posed inverse problem. This paper introduces a method for calibrating soft ERT-based tactile sensors using sim-to-real transfer learning with a finite element multiphysics model. The model is composed of three simple models that together map contact pressure distributions to voltage measurements.

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Simulation-driven Optimal Sensor Placement

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