PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping

The Effect of Bicycle Saddle Widths on Saddle Pressure in Female Cyclists

Choosing an unsuitable bicycle saddle increases the saddle pressure and discomfort during cycling. Women contract sports injuries more easily than men during cycling owing to their anatomy. To investigate the effect of saddle widths on the saddle pressure in female cyclists. Ten healthy women with an average age of 20.7 ± 1.3 years, height of 162 ± and 5.9 cm, weight of 56.1 ± 7.5 kg, and a sciatic bone width of 15.5 ± 1.4 cm were recruited for this study. The distributions of saddle pressure for four different saddle widths (i.e., narrow, moderate, wide, and self-chosen) were recorded using a saddle pressure mat. Participants were instructed to pedal steadily with a frequency of 90 RPM and a load of 150 watts. Thirty seconds of riding data was randomly retrieved for analysis. The trials were conducted with a counter-balanced design to minimize random errors. One-way repeated measures ANOVA was used to compare the saddle pressure of different saddle widths, and the significance level was set at α = 0.05. When wide saddles were used, the maximum and average pressure on the right surface of the posterior ischium were lower than those with narrow (p = 0.001, p = 0.012) and moderate (p = 0.016, p = 0.019) saddles. The area of pressure on the pubic bone was smaller when using a wide saddle than when using narrow (p = 0.005) and moderate (p = 0.018) saddles, and the area of pressure on the right posterior sciatic bone was larger under the wide saddle than under the narrow (p = 0.017) and moderate (p = 0.036) saddles. The average force was greater with the moderate saddle than with the wide (p = 0.008) and self-chosen (p = 0.025) saddles. Using a saddle with a width that is longer than the width of the cyclist's ischium by 1 cm can effectively improve the distribution of saddle pressure during riding, while providing better comfort.

New Distinct Component Patterns for Plantar Pressure Variables by Using Principal Component Analysis

BACKGROUND

It is important to determine the plantar pressure distribution of schoolchildren by applying static and dynamic foot analyses using a pedobarography device. However, it is difficult to obtain clear interpretations from results that can be explained by a large number of plantar pressure variables. The aim of this study was to use principal component analysis (PCA) to predict the main components for reducing the size of big data sets, provide a practical overview, and minimize information loss on the subject of plantar pressure assessment in youths.

METHODS

In total, 112 schoolchildren were included in the study (mean ± SD: age, 10.58 ± 1.27 years; body mass index, 18.86 ± 4.33). During the research, a pedobarography device was used to obtain plantar pressure data. Each foot was divided into six anatomical regions and evaluated. Global and regional plantar pressure distributions, load and surface areas, pressure-time integrals, weight ratios, and geometric foot properties were calculated.

RESULTS

The PCA yielded ten principal components that together account for 81.88% of the variation in the data set and represent new and distinct patterns. Thus, 137 variables affecting the subject were reduced to ten components.

CONCLUSIONS

The numerous variables that affect static and dynamic plantar pressure distributions can be reduced to ten components by PCA, making the research results more concise and understandable.

Machine-learned-based prediction of lower extremity overuse injuries using pressure plates

Although running has many benefits for both the physical and mental health, it also involves the risk of injuries which results in negative physical, psychological and economical consequences. Those injuries are often linked to specific running biomechanical parameters such as the pressure pattern of the foot while running, and they could potentially be indicative for future injuries. Previous studies focus solely on some specific type of running injury and are often only applicable to a gender or running-experience specific population. The purpose of this study is, for both male and female, first-year students, (i) to predict the development of a lower extremity overuse injury in the next 6 months based on foot pressure measurements from a pressure plate and (ii) to identify the predictive loading features. For the first objective, we developed a machine learning pipeline that analyzes foot pressure measurements and predicts whether a lower extremity overuse injury is likely to occur with an AUC of 0.639 and a Brier score of 0.201. For the second objective, we found that the higher pressures exerted on the forefoot are the most predictive for lower extremity overuse injuries and that foot areas from both the lateral and the medial side are needed. Furthermore, there are two kinds of predictive features: the angle of the FFT coefficients and the coefficients of the autoregressive AR process. However, these features are not interpretable in terms of the running biomechanics, limiting its practical use for injury prevention.