In this work, we tackled a longโstanding challenge in soft tactile sensingโaccurately localizing a contact point on a stretchable sensor even in the presence of strain and variable contact forces. Our approach uses ultrasonic scatter signals extracted from a soft waveguide to decouple these intertwined effects. A data-driven method was developed, combining:
– Global feature extraction: Using the Hilbert transform to capture the overall energy distribution before and after force contact.
– Local feature extraction: Leveraging continuous wavelet transforms (CWT) to retrieve high-resolution timeโfrequency characteristics.
– Deep learning integration: Fusing these features through a deep convolutional neural network and multilayer perceptron regression, which allowed us to achieve a mean absolute error of just 0.627 mm and a mean relative error of 3.19%.
This fusion of global and local signal analysis not only overcomes limitations of traditional time-of-flight estimation methods but also paves the way for more robust multimodal sensing in robotics and humanโmachine interfaces. The implications for advanced robotics, intelligent prosthetics, and other emerging applications are truly exciting.
Authors: Zhiheng Li, Yuan Lin, Peter Shull, Hongliang Ren
The paper is available at https://lnkd.in/dHHgw4qp
