Deep learning-based approach for high spatial resolution fibre shape
Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off
Curled fiber optic sensors, also known as fiber optic curvature sensors (FOCS), operate based on the sensitivity of light propagation to fiber bending. In a step-index optical fiber, light travels along zigzag paths within the core due to total internal reflection. When the fiber bends, some light escapes into the cladding or environment, causing a measurable change in light intensity at the fiber's end. The degree of bending, or curvature, directly affects the amount of light lost, allowing the sensor to quantify deformation or strain .
To increase sensitivity, structural modifications such as micro-trenches or etched surfaces can be applied to the fiber. These imperfections enhance light leakage when the fiber bends, enabling detection of both positive bending (convex side) and negative bending (concave side). Advanced designs may incorporate surface plasmon resonance (SPR) coatings, which allow simultaneous measurement of curvature and temperature with high accuracy .
Curled fiber optic sensors are widely used in civil engineering for monitoring bridges and buildings, in soft robotics for joint and limb movement detection, in biomedical devices for prosthetics, and in industrial systems for real-time strain and curvature monitoring . Their small size, chemical inertness, and ability to operate in harsh environments make them versatile for both research and practical applications. In summary, curled fiber optic sensors convert mechanical bending into measurable optical signals, enabling precise monitoring of curvature, strain, and environmental conditions across diverse fields.

Although the state-of-the-art fiber optic shape sensing mechanisms can provide sub-millimeter spatial resolution for off
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