通过仿真优化柔性电容传感器结构，提升触觉重建与分类精度。
Optimized Lattice-Structured Flexible EIT Sensor for Tactile Reconstruction and Classification
- 用三维场耦合仿真优化网格结构和导电层厚度，提升灵敏度。
- 触觉重建相关系数达0.9275，分类准确率高达99.6%。
- 适合可穿戴设备、机器人触觉系统等实际应用。
柔性电学阻抗断层扫描（EIT）为传统触觉传感提供了低成本、可扩展且可变形的替代方案。本文提出一种基于水凝胶导电层的优化网格结构柔性EIT触觉传感器，通过三维耦合场仿真系统设计结构参数，以提升灵敏度与鲁棒性。通过调节网格通道宽度和导电层厚度，显著改善了触觉重建质量与分类性能。实验结果表明，触觉重建相关系数最高达0.9275，信噪比峰值达29.0303 dB，结构相似性指数最高达0.9660，相对误差低至0.3798。此外，该传感器能准确分类12种不同触觉刺激，准确率达99.6%。这些结果表明，仿真引导的结构优化有助于推动柔性EIT触觉传感器在可穿戴系统、机器人及人机交互中的实际应用。
原文摘要 · Abstract (English)
Flexible electrical impedance tomography (EIT) offers a promising alternative to traditional tactile sensing approaches, enabling low-cost, scalable, and deformable sensor designs. Here, we propose an optimized lattice-structured flexible EIT tactile sensor incorporating a hydrogel-based conductive layer, systematically designed through three-dimensional coupling field simulations to optimize structural parameters for enhanced sensitivity and robustness. By tuning the lattice channel width and conductive layer thickness, we achieve significant improvements in tactile reconstruction quality and classification performance. Experimental results demonstrate high-quality tactile reconstruction with correlation coefficients up to 0.9275, peak signal-to-noise ratios reaching 29.0303 dB, and structural similarity indexes up to 0.9660, while maintaining low relative errors down to 0.3798. Furthermore, the optimized sensor accurately classifies 12 distinct tactile stimuli with an accuracy reaching 99.6%. These results highlight the potential of simulation-guided structural optimization for advancing flexible EIT-based tactile sensors toward practical applications in wearable systems, robotics, and human-machine interfaces.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。