How does a plantar scanner detect flat feet? A breakdown of the entire process of biomechanical data conversion

In-depth analysis based on 3D reconstruction and biomechanical modeling

1. Data acquisition layer: fusion of 3D imaging and pressure sensing

Optical scanning system

Using 780nm near-infrared laser array (2000dpi resolution), 150,000 feature points on the sole of the foot are captured within 0.8 seconds, and a 3D arch model with an accuracy of ±0.1mm is constructed. The standing tremor is eliminated through the dynamic compensation algorithm (automatic correction of >1.2mm amplitude).

Multimodal data synchronization

Through the timestamp calibration technology, the frame-level synchronization of 3D morphological data and dynamic pressure distribution is achieved (error <3ms), and the arch collapse process is fully recorded.

2. Data processing layer: biomechanical parameter conversion

Arch index calculation

Use the improved three-line analysis method:

Line from the medial process of the calcaneus to the first metatarsal head (L1)

Line from the fifth metatarsal head to the lateral process of the calcaneus (L2)

Vertical line of the deepest point of the arch (L3)

When the L3/L1 ratio <0.21 is judged as flat foot, the accuracy is improved by 40% compared with the traditional footprint method.

Pressure center migration rate

Calculate the COP (Center of Pressure) trajectory offset within a single gait cycle. The X-axis offset of the normal arch is ≤8mm, and that of flatfoot patients can reach 12-18mm.

Structural stiffness assessment

Based on finite element analysis, establish an arch mechanical model, input Young’s modulus (E=1.2-2.8MPa) to calculate the arch deformation resistance, and predict the risk of plantar fascia compensation.

3. Diagnostic conversion layer: clinical decision support

Dynamic grading system‌

Combining static arch height (AI<0.21) and dynamic pressure distribution (forefoot/heel pressure ratio>1.5), flat feet are divided into:

Compensatory type (reversible collapse)

Structural type (bone deformity)

Mixed type (with calcaneal valgus)‌

Orthopedic prescription generation‌

The intelligent algorithm recommends the support angle based on the arch curvature radius (R<25mm):

Inside wedge pad angle: 3-8°

Arch support height: 5-12mm

Heel cup inclination: 5-10°‌

Early warning mechanism triggering‌

When the navicular subsidence rate is detected to be >0.8mm/year or the subtalar joint pronation angle is >10°, the system automatically prompts the risk of talonavicular joint dislocation‌

IV. Technical performance comparison

Compared with traditional detection methods:

Detection dimension Scanner accuracy Visual inspection error X-ray radiation

Arch height ±0.1mm ±2.3mm 0.1mSv

Pressure peak location 0.5mm² Unquantifiable No data

Dynamic process recording 1000 frames/second Single frame recording Static image ‌

This technical system increases the sensitivity of flat foot detection to 92.7% (traditional method 68.4%) and reduces the misdiagnosis rate to 3.8% (traditional method 21.6%) ‌. 

However, it should be noted that adolescents under 18 years old need to cooperate with epiphyseal line scanning (such as EOS imaging) to exclude growth plate abnormalities ‌.