Optimized Lattice-Structured Flexible EIT Sensor for Tactile Reconstruction and Classification
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911115637686272 |
|---|---|
| author | Dong, Huazhi Teng, Sihao Han, Xu Wu, Xiaopeng Giorgio-Serchi, Francesco Yang, Yunjie |
| author_facet | Dong, Huazhi Teng, Sihao Han, Xu Wu, Xiaopeng Giorgio-Serchi, Francesco Yang, Yunjie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimized Lattice-Structured Flexible EIT Sensor for Tactile Reconstruction and Classification Dong, Huazhi Teng, Sihao Han, Xu Wu, Xiaopeng Giorgio-Serchi, Francesco Yang, Yunjie Robotics 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. |
| title | Optimized Lattice-Structured Flexible EIT Sensor for Tactile Reconstruction and Classification |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.00161 |