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纺织品表面进行触觉感知的新方法，该方法无需在感应区域内额外布置传感器，而是依赖于位于纺织品边界上的传感器。通过使用弹性针织面料和多种机器学习模型进行实验，验证了所提出方法的有效性。我们深入评估了一种特定边界传感器设计的性能。借助视觉标记，最佳视觉传感器排列在125毫米×125毫米的区域内，预测单点触控的平均均方误差为1.36毫米。此外，我们构建了一个纯纺织品原型，能够以82.85%的准确率对三种压入深度（0、15和20毫米）的触控进行分类。研究结果表明，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，为这些领域的进一步探索提供了有前景的方向。