Optical Microfiber Vector Haptic Skin for Texture Angle Information Extraction Enhancement

Fiber optic haptic skin is a class of wearable devices that mimic the structure of human skin tactile sensing system, which is gradually replacing electronic skin as a new application solution in health monitoring and human-computer interaction due to its advantages of high sensitivity and resistance to electromagnetic interference. However, most of the current fiber-optic haptic skins use a single optical fiber as a point sensing unit, which cannot accurately sense the vectorial angle information of the stress when touching complex textures. Here, we explore an enhanced structure for texture angle information extraction based on a multi-axial haptic sensing unit with dual micro-nano optical fibers, which can better differentiate between normal and axial forces, greatly expanding its potential use in the direction of haptic sensing. First, we experimentally verified the effect of the waist region crossing angle α on the vector information extraction performance of the anisotropic sensing unit, and verified that α equal to 90° is the optimal solution for the sensor structure. Further, we discuss the ability of traditional mathematical statistics and machine learning methods to construct the vector decoupling reconstruction model of force and optical power loss in this experiment through simulation and experiment. The results show that the machine learning method constructs better, and the average error is about 10% of the error value of the multiple linear regression algorithm, which is only 0.0347 dB. As a validation, we combined the machine learning algorithm to test its ability to categorize the vein textures on different leaf surfaces with an accuracy of 98.5%. This work lays an important foundation for high-precision fiber-optic texture tactile sensing.