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In medical training especially in palpation surgery, it is important for surgeons to perceive tissue stiffness. We design a novel magnetic levitation haptic device based on electromagnetic principles to enhance the perception of tissue stiffness in a virtual environment. The user can directly sense virtual tissues by moving a magnetic stylus in the magnetic field generated by the coil array of our device. To fully use the effective magnetic field, we devise an adjustable coil array and provide a reasonable explanation for such design. Moreover, we design a control interface circuit and present a self-adaptive fuzzy proportion integration differentiation (PID) algorithm to precisely control the coil current. The quantitative experiment shows that the experimental and simulation data of our device are consistent and the proposed control algorithm contributes to increasing the accuracy of tissue stiffness perception. In qualitative experiment, we recruit 22 participants to distinguish tissues of different stiffness and detect tissue abnormality. The experimental results demonstrate that our magnetic levitation haptic device can provide accurate perception of tissue stiffness.

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The elastic parameters of soft tissues are important for medical diagnosis and virtual surgery simulation. In this study, we propose a novel nonlinear parameter estimation method for soft tissues. Firstly, an in-house data acquisition platform was used to obtain external forces and their corresponding deformation values. To provide highly precise data for estimating nonlinear parameters, the measured forces were corrected using the constructed weighted combination forecasting model based on a support vector machine (WCFM_SVM). Secondly, a tetrahedral finite element parameter estimation model was established to describe the physical characteristics of soft tissues, using the substitution parameters of Young’s modulus and Poisson’s ratio to avoid solving complicated nonlinear problems. To improve the robustness of our model and avoid poor local minima, the initial parameters solved by a linear finite element model were introduced into the parameter estimation model. Finally, a self-adapting Levenberg–Marquardt (LM) algorithm was presented, which is capable of adaptively adjusting iterative parameters to solve the established parameter estimation model. The maximum absolute error of our WCFM_SVM model was less than 0.03 Newton, resulting in more accurate forces in comparison with other correction models tested. The maximum absolute error between the calculated and measured nodal displacements was less than 1.5 mm, demonstrating that our nonlinear parameters are precise.