The Relationship Between Foot Feature Parameters and Postural Stability in Healthy Subjects.

OBJECTIVE: Little has been explored about the disparate contribution of medial longitudinal arch (MLA) and lateral longitudinal arch (LLA) to human gait and postural stability. This study aims to investigate the correlation of foot feature parameters including both MLA and LLA with postural stability. METHOD: Thirteen young and healthy subjects participated in this study. The newly developed FFMS extracted foot feature parameters in nonweight-bearing (NWB) and weight-bearing (WB) conditions along with postural stability parameters in single-leg-standing (SLS) condition. A bivariate correlation analysis was carried out to investigate the correlation between the foot characteristics and the postural stability parameters. RESULTS: The foot length and width showed negative correlation with center of pressure (CoP) distance in medio-lateral (ML) and total direction, whereas the foot length in NWB and WB conditions, and the foot width in WB condition showed positive correlation with CoP distance in anterior-posterior (AP) direction. The height of the LLA curve and the area of the MLA were correlated with the postural stability parameters in AP direction. The ratios of the LLA height and area showed moderate correlation with the CoP distance in ML direction and total direction. CONCLUSION: The size of a foot, such as the length and width, is correlated with postural stability. Whereas the MLA features are associated with postural stability in AP direction, the LLA features are associated with that in ML and total direction. APPLICATION: The findings suggest that the roles and contributions of the MLA and LLA features in and to the postural control are different.

Gait Estimation from Anatomical Foot Parameters Measured by a Foot Feature Measurement System using a Deep Neural Network Model.

An accurate and credible measurement of human gait is essential in multiple areas of medical science and rehabilitation. Yet, the methods currently available are not only arduous but also costly. Researchers who investigated the relationship between foot and gait parameters have found that the two parameters are closely interrelated and suggested that measuring foot characteristics can be an alternative to the strenuous quantification currently in use. This study aims to verify the potential of foot characteristics in predicting the actual gait temporo-spatial parameters and to develop a deep neural network (DNN) model that can estimate and quantify the gait temporo-spatial parameters from foot characteristics. The foot features in sitting, standing, and one-leg standing conditions of 42 subjects were used as the input data and gait temporo-spatial parameters at fast, normal, and slow speed were set as the output of the DNN regressor. With the prediction accuracy of 95% or higher, the feasibility of the developed model was verified. This study might be the first in attempting experimental verification of the foot features serving as predictors of individual gait. The DNN regressor will help researchers improve the data pool with less labor and expense when some limitations get properly overcome.

A Foot-Arch Parameter Measurement System Using a RGB-D Camera.

The conventional method of measuring foot-arch parameters is highly dependent on the measurer's skill level, so accurate measurements are difficult to obtain. To solve this problem, we propose an autonomous geometric foot-arch analysis platform that is capable of capturing the sole of the foot and yields three foot-arch parameters: arch index (AI), arch width (AW) and arch height (AH). The proposed system captures 3D geometric and color data on the plantar surface of the foot in a static standing pose using a commercial RGB-D camera. It detects the region of the foot surface in contact with the footplate by applying the clustering and Markov random field (MRF)-based image segmentation methods. The system computes the foot-arch parameters by analyzing the 2/3D shape of the contact region. Validation experiments were carried out to assess the accuracy and repeatability of the system. The average errors for AI, AW, and AH estimation on 99 data collected from 11 subjects during 3 days were -0.17%, 0.95 mm, and 0.52 mm, respectively. Reliability and statistical analysis on the estimated foot-arch parameters, the robustness to the change of weights used in the MRF, the processing time were also performed to show the feasibility of the system.

Primary and secondary gait deviations of stroke survivors and their association with gait performance.

[Purpose] Stroke survivors exhibit abnormal pelvic motion and significantly deteriorated gait performance. Although the gait of stroke survivors has been evaluated at the primary level pertaining to ankle, knee, and hip motions, secondary deviations involving the pelvic motions are strongly related to the primary level. Therefore, the aim of this study was to identify the kinematic differences of the primary and secondary joints and to identify mechanism differences that alter the gait performance of stroke survivors. [Subjects and Methods] Five healthy subjects and five stroke survivors were recruited. All the subjects were instructed to walk at a self-selected speed. The joint kinematics and gait parameters were calculated. [Results] For the stroke survivors, the range of motion of the primary-joint motions were significantly reduced, and the secondary-joint motions were significantly increased. Additionally, for the healthy subjects, the primary joint kinematics were the main factors ensuring gait performance, whereas for the stoke survivors, the secondary-joint motions were the main factors. [Conclusion] The results indicate that while increasing the range of motion of primary-joint movements is the main target to achieve, there is a strong need to constrain and support pelvic motions in order to improve the outcome of gait rehabilitation.