Dynamic foot morphology explained through 4D scanning and shape modeling

A hybrid statistical morphometry free-form deformation approach to 3D personalized foot-ankle models

Foot and ankle joint models are widely used in the biomechanics community for musculoskeletal and finite element analysis. However, personalizing a foot and ankle joint model is highly time-consuming in terms of medical image collection and data processing. This study aims to develop and evaluate a framework for constructing a comprehensive 3D foot model that integrates statistical shape modeling (SSM) with free-form deformation (FFD) of internal bones. The SSM component is derived from external foot surface scans (skin measurements) of 50 participants, utilizing principal component analysis (PCA) to capture the variance in foot shapes. The derived surface shapes from SSM then guide the FFD process to accurately reconstruct the internal bone structures. The workflow accuracy was established by comparing three model-generated foot models against corresponding skin and bone geometries manually segmented and not part of the original training set. We used the top ten principal components representing 85 % of the population variation to create the model. For prediction validation, the average Dice similarity coefficient, Hausdorff distance error, and root mean square error were 0.92 ± 0.01, 2.2 ± 0.19 mm, and 2.95 ± 0.23 mm for soft tissues, and 0.84 ± 0.03, 1.83 ± 0.1 mm, and 2.36 ± 0.12 mm for bones, respectively. This study presents an efficient approach for 3D personalized foot model reconstruction via SSM generation of the foot surface that informs bone reconstruction based on FFD. The proposed workflow is part of the open-source Musculoskeletal Atlas Project linked to OpenSim and makes it feasible to accurately generate foot models informed by population anatomy, and suitable for rigid body analysis and finite element simulation.

Foot Posture Index Does Not Correlate with Dynamic Foot Assessment Performed via Baropodometric Examination: A Cross-Sectional Study

BACKGROUND

Clinicians employ foot morphology assessment to evaluate the functionality of the method and anticipate possible injuries. This study aims to correlate static foot posture and the dynamic barefoot evaluation in a sample of healthy adult participants.

METHODS

The foot posture was evaluated using the Foot Posture Index-6 (FPI-6) and the dynamics were evaluated through baropodometric examination. Two operators independently assessed the participants' foot posture through FPI-6, and then a dynamic evaluation was performed by asking them to walk 8 times across a platform. One hundred participants (mean age: 32.15 ± 7.49) were enrolled.

RESULTS

The inter-rater agreement between the two assessors was found to be excellent. The majority of the feet belonged to the 0 < FPI < 4 class (32%), followed by the 4 < FPI < 8 (31%) and the FPI > 8 ranges (19.5%). Our "area of contact" analysis showed a significant poor correlation between FPI and total foot, midfoot, and the second metatarsophalangeal joint (MTPJ) (-0.3 < r < 0). Regarding "force" parameters, the analysis showed a poor correlation between the midfoot, hallux, and the second toe (-0.2 < r < 2); finally the "pressure" analysis showed a poor correlation between FPI, the fourth MTPJ, and the second toe (-0.2 < rs < 0.3) and a moderate correlation between the hallux (r = 0.374) and the fifth MTPJ (r = 0.427).

CONCLUSIONS

This study emphasizes the constrained correlation between static foot posture observation and dynamic barefoot examination.

The relationship of three-dimensional foot morphology to clinical assessments and postural stability in adolescent male footballers

BACKGROUND

Foot morphology is associated with altered loading of the ankle-foot complex in adolescent footballers, predisposing to pain and injury. However, usual singular plane clinical assessments do not accurately capture the 3D nature of foot morphology. A new approach is 3D laser scanning, with statistical shape model techniques creating individual-to-group comparison. However, no research exists on the adolescent, football-playing foot. Furthermore, a link between 3D foot morphology, and usual clinical and performance measures would be beneficial for practical implementation.

METHODS

Four hundred forty-seven 3D foot scans from 224 elite male footballers (U12-U19) in bilateral stance were collected and further processed with statistical shape model techniques. Weighted shape parameters for individual principal components (Modes) were extracted for each foot. Centre of pressure displacement expressed as total travelled way in millimetres was calculated for bilateral and unilateral postural stability measures. Clinical assessments (Clarke's Angle, Resting Calcaneal Stance Position) were calculated on the 3D foot scans. Differences in weighted shape parameters, postural stability measures, and clinical assessments between age groups were determined by ANOVA. Correlations determined the relationship of Modes and clinical assessments to postural stability measures. Linear regression established if clinical assessments predicted the mode describing foot arch variation.

RESULTS

Age groups significantly differed for Mode 1 (foot length), Mode 2 (foot arch), and Mode 5 (tibial rotation relative to the foot) (p < 0.05). Resting Calcaneal Stance Position (r = .663) and Clarke's Angle (r = -.445) were low-to-moderately correlated to Mode 2 (both p < 0.001), and linear regression found they were both significant predictors of Mode 2, though only moderately (R2 = .522). There were low correlations of foot morphology to the postural stability tests.

CONCLUSION

This is the first study to describe the 3D foot morphology of male football-playing adolescents, and discover the differences between age groups. This will improve understanding and assessment of foot morphology in male adolescents because 2D techniques, as discovered in this study, do not strongly correlate to, nor predict, the 3D foot arch. Foot morphology was only lowly correlated to postural stability, thus a multifaceted program would be required for improvements.

Toward improved understanding of foot shape, foot posture, and foot biomechanics during running: A narrative review

The current narrative review has explored known associations between foot shape, foot posture, and foot conditions during running. The artificial intelligence was found to be a useful metric of foot posture but was less useful in developing and obese individuals. Care should be taken when using the foot posture index to associate pronation with injury risk, and the Achilles tendon and longitudinal arch angles are required to elucidate the risk. The statistical shape modeling (SSM) may derive learnt information from population-based inference and fill in missing data from personalized information. Bone shapes and tissue morphology have been associated with pathology, gender, age, and height and may develop rapid population-specific foot classifiers. Based on this review, future studies are suggested for 1) tracking the internal multi-segmental foot motion and mapping the biplanar 2D motion to 3D shape motion using the SSM; 2) implementing multivariate machine learning or convolutional neural network to address nonlinear correlations in foot mechanics with shape or posture; 3) standardizing wearable data for rapid prediction of instant mechanics, load accumulation, injury risks and adaptation in foot tissue and bones, and correlation with shapes; 4) analyzing dynamic shape and posture via marker-less and real-time techniques under real-life scenarios for precise evaluation of clinical foot conditions and performance-fit footwear development.