论文标题
在超声波触摸屏上进行触摸本地化的机器学习
Machine Learning for Touch Localization on Ultrasonic Wave Touchscreen
论文作者
论文摘要
研究了使用简单的深神经网络（DNN）的分类和回归，使用超声引导波在触觉表面上进行触摸定位。机器人手指首先模拟触摸动作并捕获数据以训练模型。然后用用人手指进行的实验的数据对该模型进行验证。提出了时间和频域中的定位根平方误（RMSE）。提出的方法为大多数人机相互作用提供了令人满意的定位结果，平均误差为0.47 cm，标准偏差为0.18 cm，计算时间为0.44 ms。分类方法还适用于确定访问控制键盘布局的触摸，该布局的精度为97％，计算时间为0.28 ms。这些结果表明，基于DNN的方法是基于信号处理的方法的可行替代方法，可使用超声波引导波进行准确和鲁棒的触摸定位。
Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A robotic finger first simulates the touch action and captures the data to train a model. The model is then validated with data from experiments conducted with human fingers. The localization root mean square errors (RMSE) in time and frequency domains are presented. The proposed method provides satisfactory localization results for most human-machine interactions, with a mean error of 0.47 cm and standard deviation of 0.18 cm and a computing time of 0.44 ms. The classification approach is also adapted to identify touches on an access control keypad layout, which leads to an accuracy of 97% with a computing time of 0.28 ms. These results demonstrate that DNN-based methods are a viable alternative to signal processing-based approaches for accurate and robust touch localization using ultrasonic guided waves.
