Faculty of Engineering, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada.
All Waves Technologies, Sherbrooke, QC J1N 0C8, Canada.
Sensors (Basel). 2022 Apr 21;22(9):3183. doi: 10.3390/s22093183.
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.
采用简单的深度神经网络（DNN）进行分类和回归，以利用超声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来训练模型。然后使用人类手指进行的实验数据验证模型。给出了时间和频率域中的定位均方根误差（RMSE）。对于大多数人机交互，所提出的方法提供了令人满意的定位结果，平均误差为 0.47 厘米，标准偏差为 0.18 厘米，计算时间为 0.44 毫秒。分类方法也适用于识别访问控制键盘布局上的触摸，其计算时间为 0.28 毫秒，准确率为 97%。这些结果表明，基于 DNN 的方法是一种可行的替代信号处理方法，可用于使用超声导波进行准确和鲁棒的触摸定位。
