This study presents a novel approach for touch sensing using semi-elastic textile surfaces that does not require the placement of additional sensors in the sensing area, instead relying on sensors located on the border of the textile. The proposed approach is demonstrated through experiments involving an elastic Jersey fabric and a variety of machine-learning models. The performance of one particular border-based sensor design is evaluated in depth. By using visual markers, the best-performing visual sensor arrangement predicts a single touch point with a mean squared error of 1.36 mm on an area of 125mm by 125mm. We built a textile only prototype that is able to classify touch at three indent levels (0, 15, and 20 mm) with an accuracy of 82.85%. Our results suggest that this approach has potential applications in wearable technology and smart textiles, making it a promising avenue for further exploration in these fields.

翻译：本研究提出了一种利用半弹性织物表面进行触觉感知的新方法，该方法无需在感知区域布置额外传感器，而是依赖位于织物边界的传感器实现。通过使用弹性针织面料及多种机器学习模型进行实验，验证了所提方法的有效性。我们深入评估了一种特定边界传感器设计的性能。通过引入视觉标记，性能最佳的视觉传感器布局在125mm×125mm区域内对单点触控的预测均方误差达1.36毫米。我们构建的纯织物原型能够以82.85%的准确率区分三种按压深度（0、15和20毫米）。实验结果表明，该方法在可穿戴技术和智能纺织品领域具有潜在应用价值，为相关领域的进一步探索提供了有前景的研究方向。