How to accurately analyze the arch morphology through a foot 3D scanner?
- High-precision 3D modeling technology
The accurate analysis of the arch morphology depends on the complete digital reconstruction of the three-dimensional structure of the foot. Modern foot scanners use multimodal optical fusion technology to capture the surface features of the arch with millimeter-level accuracy through the collaborative work of structured light and laser scanning:
Structured light scanning: Project a coded grating pattern onto the foot and capture the deformation of the light stripes through the camera. The algorithm calculates the surface curvature of the arch according to the degree of light bending. A single scan can obtain more than 500,000 3D coordinate points, accurately recording the changes in the arch height and curvature.
Laser point cloud supplement: For the concave area on the inside of the arch, the 780nm laser beam scans point by point at a spacing of 0.02mm to eliminate the shadow blind spots that may be generated by structured light, and fully present the spatial positions of key supporting structures such as the navicular bone and cuneiform bone.
Dynamic posture calibration: The subject needs to stand barefoot on a transparent stage, and the bottom arch morphology is captured through mirror reflection at the bottom. Combined with the data of four wide-angle lenses at the top, a 360° three-dimensional model without blind spots is constructed.
- Arch parameter quantification system
The built-in geometric topological analysis algorithm of the scanner extracts 12 core arch parameters from the three-dimensional model:
Arch height: The vertical distance from the calcaneal tuberosity to the first metatarsal head, with an accuracy of ±0.3mm, distinguishes normal feet (15-18mm), flat feet (<12mm) and high arch feet (>20mm).
Arch angle: Calculate the spatial angle between the medial longitudinal arch (calcaneus-navicular-first metatarsal) and the lateral longitudinal arch (calcaneus-cuboid-fifth metatarsal) to evaluate the mechanical stability of the arch.
Volume index: Quantify the volume of the arch depression area through triangular mesh segmentation technology, and judge the degree of collapse in combination with the foot length ratio.
Curvature radius: NURBS surface is used to fit the arch surface, the radius value of the maximum curvature point is calculated, and abnormal protrusions or depressions are identified.
III. Intelligent classification and biomechanical modeling
The system automatically classifies the arch morphology through the deep learning model:
Feature comparison: The scan data is matched with the million-level foot database, and the arch morphology feature vector is extracted based on principal component analysis (PCA), with an accuracy rate of up to 95%.
Mechanical deduction: Even if the pressure is not measured directly, the finite element model can be constructed through the arch geometry parameters. The algorithm simulates the stress distribution of the arch under load and predicts the plantar fascia tension changes and joint wear risks.
Dynamic monitoring: The new scanner supports continuous shooting function, capturing the arch deformation process from heel contact to push-off period, and quantifying the elastic deformation rate and energy absorption efficiency during movement.
IV. Clinical and sports science applications
Customized correction: 3D print orthopedic insoles based on arch parameters, design cushioning cavities for high arched feet, and add medial wedge support for flat feet, with the error controlled within 0.5mm.
Sports equipment optimization: The midsole density distribution of marathon running shoes can be adjusted according to the individual arch curvature to reduce plantar fascia damage during long-distance running.
Early warning of lesions: By monitoring the annual change rate of arch height (>2mm/year), degenerative diseases such as posterior tibial tendon dysfunction can be detected 6-12 months in advance.
