Fig. 4: Flowchart illustrating the process of machine learning-based force decoupling system within a single image frame. | Nature Communications

Fig. 4: Flowchart illustrating the process of machine learning-based force decoupling system within a single image frame.

From: Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Fig. 4

a Captured non-axisymmetric luminescence signal induced by dynamic shear force (top) and pressure distribution profile (bottom) (Scale bar, 2 mm). b The process of feature engineering-based standard threshold estimation for force decoupling. Tendentious features of the RGB color space under applied normal force are quantified and utilized to train an SVR model for normal force estimation. Subsequently, the standard threshold for extracting the normal force region is determined by another SVR, which was trained on intensity profiles corresponding to the applied normal force. The estimated threshold and its half value were employed to delineate the normal and shear force regions, respectively. c Demonstration of decoupled normal (left) and shear force (right) in luminescence images (top) and pressure distribution profile (bottom). d Real-time decoupled estimation from a single frame of a non-axisymmetric luminescence signal. Source data are provided as a Source Data file.

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