一种结合非线性动力学、感知心理学和高频触觉渲染的高保真感知优化手术模拟框架
A Koopman-Bayesian Framework for High-Fidelity, Perceptually Optimized Haptic Surgical Simulation
AI总结：
本文提出一种结合非线性动力学、感知心理学和高频触觉渲染的框架，通过贝叶斯校准提升手术模拟的真实感和感知准确性。
摘要：
我们介绍了一个统一的框架，结合非线性动力学、感知心理学和高频触觉渲染，以增强手术模拟的真实性。将手术设备与软组织的相互作用提升到一个增强的状态空间中，通过Koopman算子公式，允许对本质上非线性的动力学进行线性预测和控制。为了使渲染的力与人类感知极限一致，我们提出了一种基于Weber-Fechner和Stevens缩放定律的贝叶斯校准模块，逐步将力信号与每个个体的辨别阈值相对应。对于各种模拟的手术任务，如触诊、切口和骨铣削，所提出的系统实现了平均渲染延迟为4.3毫秒，力误差小于2.8%，以及在感知辨别方面提高了20%。多变量统计分析（MANOVA和回归）表明，系统性能显著优于传统弹簧阻尼和能量基于的渲染方法。最后，我们讨论了该系统对手术培训和VR医学教育的潜在影响，并勾勒了未来工作方向，即向闭合的神经反馈回路在触觉接口中的发展。
英文摘要：
We introduce a unified framework that combines nonlinear dynamics, perceptual psychophysics and high frequency haptic rendering to enhance realism in surgical simulation. The interaction of the surgical device with soft tissue is elevated to an augmented state space with a Koopman operator formulation, allowing linear prediction and control of the dynamics that are nonlinear by nature. To make the rendered forces consistent with human perceptual limits, we put forward a Bayesian calibration module based on WeberFechner and Stevens scaling laws, which progressively shape force signals relative to each individual's discrimination thresholds. For various simulated surgical tasks such as palpation, incision, and bone milling, the proposed system attains an average rendering latency of 4.3 ms, a force error of less than 2.8% and a 20% improvement in perceptual discrimination. Multivariate statistical analyses (MANOVA and regression) reveal that the system's performance is significantly better than that of conventional spring-damper and energy, based rendering methods. We end by discussing the potential impact on surgical training and VR, based medical education, as well as sketching future work toward closed, loop neural feedback in haptic interfaces.
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