<br>支持机器学习的触觉传感器设计，用于动态触摸解码,Advanced Science

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支持机器学习的触觉传感器设计，用于动态触摸解码
Advanced Science ( IF 14.3 ) Pub Date : 2023-09-22 , DOI: 10.1002/advs.202303949
Yuyao Lu ₁ , Depeng Kong ₁ , Geng Yang _{1,

2} , Ruohan Wang ₁ , Gaoyang Pang ₃ , Huayu Luo ₁ , Huayong Yang ₁ , Kaichen Xu ₁

Affiliation

类似皮肤的柔性传感器在医疗保健和人机交互中发挥着至关重要的作用。然而，总体目标侧重于追求类皮肤传感器本身的内在静态和动态性能，并进行各种试错尝试。这种前向策略几乎将传感器的设计与最终的应用隔离开来。在此，报告了一种机器学习（ML）引导的柔性触觉传感器系统设计，能够在六种动态触摸模式中实现触觉感知的高分类精度（约99.58％）。与直觉驱动的传感器设计不同，这种机器学习引导的性能优化是通过引入基于支持向量机的机器学习算法以及制造参数选择的特定统计标准来挖掘原始传感数据中隐藏的特征来实现的。这种逆向设计将统计学习标准合并到传感硬件的设计阶段，弥合了设备结构和算法之间的差距。使用优化的触觉传感器，可以获得手写应用中的高质量可识别信号。此外，通过额外的数据处理，装配有传感器的机械手能够高精度地完成11位盲文电话号码的实时触摸解码。

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Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding

Skin-like flexible sensors play vital roles in healthcare and human–machine interactions. However, general goals focus on pursuing intrinsic static and dynamic performance of skin-like sensors themselves accompanied with diverse trial-and-error attempts. Such a forward strategy almost isolates the design of sensors from resulting applications. Here, a machine learning (ML)-guided design of flexible tactile sensor system is reported, enabling a high classification accuracy (≈99.58%) of tactile perception in six dynamic touch modalities. Different from the intuition-driven sensor design, such ML-guided performance optimization is realized by introducing a support vector machine-based ML algorithm along with specific statistical criteria for fabrication parameters selection to excavate features deeply concealed in raw sensing data. This inverse design merges the statistical learning criteria into the design phase of sensing hardware, bridging the gap between the device structures and algorithms. Using the optimized tactile sensor, the high-quality recognizable signals in handwriting applications are obtained. Besides, with the additional data processing, a robot hand assembled with the sensor is able to complete real-time touch-decoding of an 11-digit braille phone number with high accuracy.

更新日期：2023-09-22

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