Machine Learning-Enabled Tactile Sensor Design for Dynamic Touch Decoding.

ABSTRACT: 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 featur

SUBMITTER: Lu Y 

PROVIDER: S-EPMC10646241 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature