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.

使用半弹性纺织品表面的新方法，通过在纺织品边界上的传感器而不是感测区域上的传感器进行触摸感测，结合多种机器学习模型进行实验证明其可行性，特别是使用视觉标记预测单一触摸点的性能表现优秀，平均均方误差仅为1.36毫米，所开发的纺织品原型能准确分类3个深度的触摸，表明该方法在可穿戴技术和智能纺织品方面具有潜在应用价值，并值得进一步研究探讨。

基于边界传感器的半弹性纺织品触控感应