Classification of forefoot pain based on plantar pressure measurements

Plantar pressure in relation to hindfoot varus in people with unilateral upper motor neuron syndrome

INTRODUCTION

Hindfoot varus deformity is common in people with unilateral upper motor neuron syndrome (UMNS) and can be dynamic or persistent. The aims of this study were (1) to gain insight into plantar pressure characteristics of people with chronic UMNS in relation to hindfoot varus and (2) to propose a quantitative outcome measure, based on plantar pressure, for the scientific evaluation of surgical interventions.

METHODS

In this retrospective study, a cohort comprising plantar pressure data of 49 people with UMNS (22 "no hindfoot varus", 18 "dynamic hindfoot varus", and 9 "persistent hindfoot varus"), and 586 healthy controls was analyzed. As an indication of plantigrade foot contact, the ratio between the plantar contact area of the affected and the non-affected foot was calculated. To investigate spatial and temporal aspects of plantar pressure, normalized plantar pressure patterns and center of pressure trajectories were computed.

RESULTS

People with UMNS had lower plantar pressure area ratios compared to healthy controls. Additionally, increased plantar pressure underneath the lateral foot was found in people with a persistent hindfoot varus. Center of pressure trajectories were more lateral during the first 26% of the stance phase in people with a dynamic hindfoot varus and during the first 82% of the stance phase in people with a persistent hindfoot varus compared to healthy controls.

CONCLUSION

Spatial and temporal differences in plantar pressure were found in people with dynamic or persistent hindfoot varus deformity. We propose to primarily use the medio-lateral center of pressure trajectory as outcome measure for the scientific evaluation of surgical interventions targeting hindfoot varus.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

Artificial intelligence in foot and ankle surgery: current concepts

The twenty-first century has proven that data are the new gold. Artificial intelligence (AI) driven technologies might potentially change the clinical practice in all medical specialities, including orthopedic surgery. AI has a broad spectrum of subcomponents, including machine learning, which consists of a subdivision called deep learning. AI has the potential to increase healthcare delivery, improve indications and interventions, and minimize errors. In orthopedic surgery. AI supports the surgeon in the evaluation of radiological images, training of surgical residents, and excellent performance of machine-assisted surgery. The AI algorithms improve the administrative and management processes of hospitals and clinics, electronic healthcare databases, monitoring the outcomes, and safety controls. AI models are being developed in nearly all orthopedic subspecialties, including arthroscopy, arthroplasty, tumor, spinal and pediatric surgery. The present study discusses current applications, limitations, and future prospective of AI in foot and ankle surgery.

Quantitative assessment of plantar pressure patterns in relation to foot deformities in people with hereditary motor and sensory neuropathies

BACKGROUND

Hereditary motor and sensory neuropathies (HMSN), also known as Charcot-Marie-Tooth disease, are characterized by affected peripheral nerves. This often results in foot deformities that can be classified into four categories: (1) plantar flexed first metatarsal, neutral hindfoot, (2) plantar flexed first metatarsal, correctable hindfoot varus, (3) plantar flexed first metatarsal, uncorrectable hindfoot varus, and (4) hindfoot valgus. To improve management and for the evaluation of surgical interventions, a quantitative evaluation of foot function is required. The first aim of this study was to provide insight into plantar pressure of people with HMSN in relation to foot deformities. The second aim was to propose a quantitative outcome measure for the evaluation of surgical interventions based on plantar pressure.

METHODS

In this historic cohort study, plantar pressure measurements of 52 people with HMSN and 586 healthy controls were evaluated. In addition to the evaluation of complete plantar pressure patterns, root mean square deviations (RMSD) of plantar pressure patterns from the mean plantar pressure pattern of healthy controls were calculated as a measure of abnormality. Furthermore, center of pressure trajectories were calculated to investigate temporal characteristics. Additionally, plantar pressure ratios of the lateral foot, toes, first metatarsal head, second/third metatarsal heads, fifth metatarsal head, and midfoot were calculated to measure overloading of foot areas.

RESULTS

Larger RMSD values were found for all foot deformity categories compared to healthy controls (p < 0.001). Evaluation of the complete plantar pressure patterns revealed differences in plantar pressure between people with HMSN and healthy controls underneath the rearfoot, lateral foot, and second/third metatarsal heads. Center of pressure trajectories differed between people with HMSN and healthy controls in the medio-lateral and anterior-posterior direction. The plantar pressure ratios, and especially the fifth metatarsal head pressure ratio, differed between healthy controls and people with HMSN (p < 0.05) and between the four foot deformity categories (p < 0.05).

CONCLUSIONS

Spatially and temporally distinct plantar pressure patterns were found for the four foot deformity categories in people with HMSN. We suggest to consider the RMSD in combination with the fifth metatarsal head pressure ratio as outcome measures for the evaluation of surgical interventions in people with HMSN.

Advancements in Artificial Intelligence for Foot and Ankle Surgery: A Systematic Review

Background

There has been a rapid increase in research applying artificial intelligence (AI) to various subspecialties of orthopaedic surgery, including foot and ankle surgery. The purpose of this systematic review is to (1) characterize the topics and objectives of studies using AI in foot and ankle surgery, (2) evaluate the performance of their models, and (3) evaluate their validity (internal or external validation).

Methods

A systematic literature review was conducted using PubMed/MEDLINE and Embase databases in December 2022. All studies that used AI or its subsets machine learning (ML) and deep learning (DL) in the setting of foot and ankle surgery relevant to orthopaedic surgeons were included. Studies were evaluated for their demographics, subject area, outcomes of interest, model(s) tested, model(s)' performance, and validity (internal or external).

Results

A total of 31 studies met inclusion criteria: 14 studies investigated AI for image interpretation, 13 studies investigated AI for clinical predictions, and 4 studies were grouped as "other." Studies commonly explored AI for ankle fractures, calcaneus fractures, hallux valgus, Achilles tendon pathologies, plantar fasciitis, and sports injuries. For studies reporting the area under the receiver operating characteristic curve (AUC), AUCs ranged from 0.64 (poor) to 0.99 (excellent). Two studies (6.45%) reported external validation.

Conclusion

Applications of AI in the field of foot and ankle surgery are expanding, particularly for image interpretation and clinical predictions. Current model performances range from poor to excellent, and most studies lack external validation, demonstrating a need for further research prior to deploying AI-based clinical applications.

Level of Evidence

Level III, retrospective cohort study.

Pressure Properties of Three Different Types of Prefabricated Insoles Related to Plantar Pressure in Asymptomatic Men

BACKGROUND

In clinical fields, many types of insoles are used to not only realign movement patterns, but also treat pressure-related foot diseases. However, the characteristics of and plantar pressure in each type of insole are still unclear. Therefore, the aim of this study was to validate the plantar pressure-relieving effect of three representative types of insoles (metatarsal padding insole [MPI], lateral heel wedge insole [LHI], and arch support insole [ASI]) in asymptomatic men.

METHODS

A total of 35 feet of 35 asymptomatic men with a mean age of 23.4 ± 2.0 years were included. Pedobarographic data were evaluated by dividing the foot into eight designated regions to compare the three types of insoles. Peak plantar pressure (PPP) and pressure time integral (PTI) were assessed using the Pedar-X system. A repeated measures analysis of variance was used for statistical analyses.

RESULTS

In the hallux region, there was no statistically significant difference. MPI showed highest pressure in the 2nd-5th toe and midfoot region, but lowest in the central and lateral forefoot regions. Meanwhile, ASI showed highest pressure in the medial forefoot region but lowest in the lateral heel region. Lastly, pressure in the lateral heel region was highest in LHI. Overall, results of PTI were similar to those of PPP.

CONCLUSIONS

This study demonstrated that the three types of insole each could reduce and redistribute pressure of specific part of the foot to help select an appropriate insole for each purpose.

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.

The Effect of Foot Deformity and First Metatarsophalangeal Joint Plantar Pain on Performance in DanceSport Athletes

Background: Adolescent DanceSport athletes who regularly dance in high heels have a higher possibility of developing hallux valgus deformity and foot pain. We believe that the occurrence of foot disorders may change the loading on their feet, which thus affects the athletic performance of those adolescents. Methods: A total of 63 adolescent DanceSport athletes (16 boys, 47 girls) were included. The plantar pain in the first metatarsophalangeal (1st MTP) joint was evaluated using a questionnaire, and the hallux valgus angle was evaluated using digital photographs (HVAp). The loading values of the plantar pressure while performing relève on demi-pointe were measured using sensor sheets. The participating boys and girls were analyzed separately. Results: The results showed that female adolescent DanceSport athletes with the 1st MTP joint plantar pain showed a decrease in the loading distribution and plantar pressure percentage on the hallux and an increased loading distribution and pressure distribution of the metatarsal head as the HVAp increased. Conclusion: Among adolescent DanceSport athletes with plantar pain in the 1st MTP joint and a large HVAp, the loading manner of the foot may have changed, which may be associated with a decrease in the toe function and performance.

MODELING OF PATELLA HEIGHT WITH DISTAL FEMUR AND PROXIMAL TIBIA REFERENCE POINTS WITH ARTIFICIAL NEURAL NETWORK

The patellofemoral joint is one of the parts of the knee extension mechanism that plays a role in the stability of the knee by enlarging the force arm of the quadriceps muscle and changing the direction of the muscle strength. For the entire knee joint to perform its task painlessly and functionally, the positions and strength of the muscles, the strength of the ligaments, and their reaction to movement must be compatible. The Insall–Salvati (Ins-Sal) index is useful for showing changes in patellar height produced by repositioning the tibial plateau, in other words, showing changes in patellar tendon length. Patella height is an important value to be taken into account in knee prosthesis surgery, tibial osteotomy, and anterior cruciate ligament reconstruction. The morphometric relationship between the reference measurements of the distal femur and proximal tibia and the position of the patella will be useful in determining the natural anatomy. In this study, we aimed to determine the relationship between patella height and distal femur and proximal tibia reference areas by using the artificial neural network method as an alternative approach method. In order to assess the performance of the estimation of the Ins-Sal index, the four ANN model with six input combinations which included age, gender and the reference measurements for the right and left sides have been constructed and tested. The MSE and [Formula: see text] values are calculated for every four models for the training and test phase. The results show that the proposed approach for modeling of relation between reference measurements and the Ins-Sal index is a powerful approach.

Mathematical and computational models for pain: a systematic review

OBJECTIVE

There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain.

METHODS

The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant.

RESULTS

Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment.

CONCLUSIONS

Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.

Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network

Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.

Diagnosis of Patellofemoral Pain Syndrome Based on a Multi-Input Convolutional Neural Network With Data Augmentation

Patellofemoral pain syndrome (PFPS) is a common disease of the knee. Despite its high incidence rate, its specific cause remains unclear. The artificial neural network model can be used for computer-aided diagnosis. Traditional diagnostic methods usually only consider a single factor. However, PFPS involves different biomechanical characteristics of the lower limbs. Thus, multiple biomechanical characteristics must be considered in the neural network model. The data distribution between different characteristic dimensions is different. Thus, preprocessing is necessary to make the different characteristic dimensions comparable. However, a general rule to follow in the selection of biomechanical data preprocessing methods is lacking, and different preprocessing methods have their own advantages and disadvantages. Therefore, this paper proposes a multi-input convolutional neural network (MI-CNN) method that uses two input channels to mine the information of lower limb biomechanical data from two mainstream data preprocessing methods (standardization and normalization) to diagnose PFPS. Data were augmented by horizontally flipping the multi-dimensional time-series signal to prevent network overfitting and improve model accuracy. The proposed method was tested on the walking and running datasets of 41 subjects (26 patients with PFPS and 15 pain-free controls). Three joint angles of the lower limbs and surface electromyography signals of seven muscles around the knee joint were used as input. MI-CNN was used to automatically extract features to classify patients with PFPS and pain-free controls. Compared with the traditional single-input convolutional neural network (SI-CNN) model and previous methods, the proposed MI-CNN method achieved a higher detection sensitivity of 97.6%, a specificity of 76.0%, and an accuracy of 89.0% on the running dataset. The accuracy of SI-CNN in the running dataset was about 82.5%. The results prove that combining the appropriate neural network model and biomechanical analysis can establish an accurate, convenient, and real-time auxiliary diagnosis system for PFPS to prevent misdiagnosis.

Effects of Artificial Texture Insoles and Foot Arches on Improving Arch Collapse in Flat Feet

The arches of the foot play a vital role in cushioning the impact and pressure generated from ground reaction forces due to body weight. Owing to a lack of normal human arch structure, people diagnosed as having flat feet often have discomfort in the soles of their feet. The results may not only cause inappropriate foot pressure distribution on the sole but also further cause foot injuries. This study heavily relies on a homemade foot pressure sensing device equipped with textured insoles of different heights and artificial arches. This was to explore the extent to which the pressure distribution of the foot in people with flat feet could be improved. A further comparison was made of the effects of using the textured insoles with different heights on two different groups of people diagnosed with flat and normal feet respectively. Sixty-five undergraduate and postgraduate volunteers were invited to receive the ink footprint test for measuring their degrees of arch index. Nine of these 65 had 2 flat feet, 3 had a left flat foot, 5 had a right flat foot, and 48 had 2 normal feet. To ensure the same number of subjects in both the control and the experimental groups, 9 of the 48 subjects who had normal feet were randomly selected. In total, 26 subjects (Male: 25, Female: 1; Age: 22 ± 1 years; height: 173.6 ± 2.5 cm; body mass: 68.3 ± 5.4 kg; BMI: 22.6 ± 1.2) were invited to participate in this foot pressure sensing insoles study. The experimental results showed that the use of textured insoles designed with different heights could not effectively improve the plantar pressure distribution and body stability in subjects with flat feet. Conversely, the use of an artificial arch effectively improved the excessive peak in pressure and poor body stability, and alleviated the problem of plantar collapse for patients with flat feet, especially in the inner part of their hallux and forefoot.

Predicting foot orthosis deformation based on its contour kinematics during walking

BACKGROUND

Customized foot orthoses (FOs) are designed based on foot posture and function, while the interaction between these metrics and FO deformation remains unknown due to technical problems. Our aim was to predict FO deformation under dynamic loading using an artificial intelligence (AI) approach, and to report the deformation of two FOs of different stiffness during walking.

METHODS

Each FO was fixed on a plate, and six triad reflective markers were fitted on its contour, and 55 markers on its plantar surface. Manual loadings with known magnitude and application point were applied to deform "sport" and "regular" (stiffer) FOs in all regions (training session). Then, 13 healthy male subjects walked with the same FOs inside shoes, where the triad markers were visible by means of shoe holes (walking session). The marker trajectories were recorded using optoelectronic system. A neural network was trained to find the dependency between the orientation of triads on FO contour and the position of markers on its plantar surface. After tuning hyperparameters and evaluating the performance of the model, marker positions on FOs surfaces were predicted during walking for each subject. Statistical parametric mapping was used to compare the pattern of deformation between two FOs.

RESULTS

Overall, the model showed an average error of <0.6 mm for predicting the marker positions on both FOs. The training setup was appropriate to simulate the range of triads' displacement and the peak loading on FOs during walking. Sport FO showed different pattern and significantly higher range of deformation during walking compared to regular FO.

CONCLUSION

Our technique enables an indirect and accurate estimation of FO surface deformation during walking. The AI model was capable to make a distinction between two FOs with different stiffness and between subjects. This innovative approach can help to optimally customize the FO design.

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

Quantitative analyses of plantar pressure images typically occur at the group level and under the assumption that individuals within each group display homogeneous pressure patterns. When this assumption does not hold, a personalized analysis technique is required. Yet, existing personalized plantar pressure analysis techniques work at the image level, leading to results that can be unintuitive and difficult to interpret. To address these limitations, we introduce PAPPI: the Personalized Analysis of Plantar Pressure Images. PAPPI is built around the statistical modelling of the relationship between plantar pressures in healthy controls and their demographic characteristics. This statistical model then serves as the healthy baseline to which an individual’s real plantar pressures are compared using statistical parametric mapping. As a proof-of-concept, we evaluated PAPPI on a cohort of 50 hallux valgus patients. PAPPI showed that plantar pressures from hallux valgus patients did not have a single, homogeneous pattern, but instead, 5 abnormal pressure patterns were observed in sections of this population. When comparing these patterns to foot pain scores (i.e. Foot Function Index, Manchester-Oxford Foot Questionnaire) and radiographic hallux angle measurements, we observed that patients with increased pressure under metatarsal 1 reported less foot pain than other patients in the cohort, while patients with abnormal pressures in the heel showed more severe hallux valgus angles and more foot pain. Also, incidences of pes planus were higher in our hallux valgus cohort compared to the modelled healthy controls. PAPPI helped to clarify recent discrepancies in group-level plantar pressure studies and showed its unique ability to produce quantitative, interpretable, and personalized analyses for plantar pressure images.

Plantar pressure improvement in moderate hallux valgus with modified chevron osteotomy: Clinical and radiographic outcomes

BACKGROUND

Hallux valgus (HV) is widely treated by Chevron osteotomy (CO); however, a modified CO may improve patient outcomes and recovery.

METHODS

A prospective study was designed to analyze plantar pressure measurements and clinical and radiographic outcomes of a modified CO for HV. Recruitment was between February 2016 and February 2017.

INCLUSION CRITERIA

diagnosis of moderate HV; an indication for surgical correction due to discomfort, pain or difficulty with shoe wear; and age over 18 years. Clinical and radiographic outcomes were evaluated using the American Orthopedic Foot and Ankle Society (AOFAS) guidelines and a visual analog scale (VAS).

RESULTS

Forty-four patients met inclusion criteria. After surgery, the highest percentage in mean pressure was in the first and fifth metatarsal heads. At 12 months' follow-up, the AOFAS score improved, but differences in VAS scale were only significant at baseline.

CONCLUSIONS

Modified CO is a good option for people with HV, improving foot activity compared to preoperative levels while limiting the time needed for recovery.

Foot Function in Patients With Surgically Treated Preaxial Polydactyly of the Foot Compared With Age- and Sex-Matched Healthy Controls

BACKGROUND:

Treatment of preaxial foot polydactyly, a duplication of the first ray, consists of excision of an extra ray, aiming to improve shoe fitting and aesthetic appearance, while maintaining foot function. Currently, the effect of excision on foot function and foot-related patient experiences is unclear.

METHODS:

A cross-sectional comparison between 37 children treated for preaxial foot polydactyly and 37 age- and sex-matched healthy controls was performed. Dynamic foot function was assessed using plantar pressure measurements and static foot characteristics by physical examination. Patient-reported outcomes for foot function and footwear were evaluated, using the Oxford Ankle Foot Questionnaire for Children (score, 0-100).

RESULTS:

Compared with controls, patients had significantly lower median peak pressures at the hallux (148 kPa [IQR, 98-245] vs 272 kPa [IQR, 205-381], P < .001) and significantly higher peak pressures at the second metatarsal (217 kPa [IQR, 147-338] vs 166 kPa [IQR, 141-235], P = .002) and third to fifth metatarsals (214 kPa [IQR, 147-290] vs 161 kPa [IQR, 135-235], P < .001). Additionally, patients had a more medially deviated hallux, both while seated (15 degrees (IQR, 11-20) vs 12 degrees [IQR, 10-15], P = .001) and standing (20 degrees [IQR, 15-26] vs 18 degrees [IQR, 15-20], P = .001). No significant correlation between peak pressure distribution and hallux deviation was found. Patients reported minimal problems with foot function (87.5 [IQR, 64.6-100]), but distinct problems with footwear use (50.0 [IQR, 25.0-100]).

CONCLUSION:

Patients with surgically treated preaxial foot polydactyly had a substantially altered plantar pressure distribution with more lateral foot progression than healthy controls. Although an increased hallux deviation was not related to altered foot function, it seemed to be the reason for the patient-perceived problems with footwear. Level of Evidence: Level III, comparative series.

Distribution of the Highest Plantar Pressure Regions in Patients with Diabetes and Its Association with Peripheral Neuropathy, Gender, Age, and BMI: One Centre Study

The abnormal plantar pressure distribution and value play a key role in the formation of plantar calluses and diabetic foot ulcer. The prevalence of the highest pressure different distribution and its association with various factors among patients with diabetes is not well known. The study purpose was to evaluate the prevalence of different regions for the highest pressure on the sole and its association with selected factors among patients with diabetes. Medical records of nonulcer patients were retrospectively analysed. The relationship between pressure patterns on the sole obtained during a pedobarographic test as a semiquantitative assessment with colourful print analysis and neuropathy, gender, age, and BMI was searched. The most common location of the highest pressure was the central part of the forefoot. No association was found between the different highest pressure regions and age, sensory neuropathy, calluses, and foot deformities. The highest pressure on the lateral part of the foot and midfoot was observed more often in females and in patients with a BMI ≥ 35. The prevalence of the highest pressure on the forefoot was more common in patients with a BMI < 35. Conclusions. The most frequent regions of the highest pressure on the sole in patients with diabetes were the central part of the forefoot (2-3 metatarsal heads) with no simple relationship to the assessed variables other than BMI < 35. Female gender and higher BMI seem to be responsible for shifting the place of the highest pressure to other places of the foot.

Do Strike Patterns or Shoe Conditions have a Predominant Influence on Foot Loading?

This study aimed to explore the effects of strike patterns and shoe conditions on foot loading during running. Twelve male runners were required to run under shoe (SR) and barefoot conditions (BR) with forefoot (FFS) and rearfoot strike patterns (RFS). Kistler force plates and the Medilogic insole plantar pressure system were used to collect kinetic data. SR with RFS significantly reduced the maximum loading rate, whereas SR with FFS significantly increased the maximum push-off force compared to BR. Plantar pressure variables were more influenced by the strike patterns (15 out of 18 variables) than shoe conditions (7 out of 18 variables). The peak pressure of midfoot and heel regions was significantly increased in RFS, but appeared in a later time compared to FFS. The influence of strike patterns on running, particularly on plantar pressure characteristics, was more significant than that of shoe conditions. Heel-toe running caused a significant impact force on the heel, whereas wearing cushioned shoes significantly reduced the maximum loading rate. FFS running can prevent the impact caused by RFS. However, peak plantar pressure was centered at the forefoot for a long period, thereby inducing a potential risk of injury in the metatarsus/phalanx.

Differences in foot contact times between obese and non-obese postmenopausal women when crossing obstacles

OBJECTIVE

This study aimed to investigate the foot contact time differences between obese and non-obese subjects during walking when crossing obstacles.

METHODS

Ninety-eight postmenopausal women were assigned to four groups, and their plantar pressure temporal data were collected using a two-step protocol during walking when crossing an obstacle set at 30% height of lower limb length of each subject. The initial, final, and duration of contact of 10 foot areas were measured.

RESULTS

Leading limb: (1) the heel groups initiated foot contact using the heel, and the non-heel groups initiated contact using the metatarsals; (2) heel obese subjects showed an earlier initial contact and a longer contact duration of metatarsals 2-3; (3) non-heel obese subjects showed an earlier midfoot initial contact. Regarding the trailing limb: (4) heel obese subjects showed an earlier midfoot initial contact and a longer contact duration of metatarsal 5; (5) non-heel obese subjects showed an earlier initial contact and a longer contact duration of metatarsals 4-5.

CONCLUSIONS

(1) The non-heel groups' foot rollover pattern may result from an attempt of rapidly restoring stability; (2) the heel obese subjects seem to regulate their plantar foot muscles to overcome their overweight; (3) the overweight of the non-heel obese subjects leads to a quicker backward foot roll-over from the metatarsals to the heel; (4) the overweight of the heel obese subjects can distort their footprints and/or their higher inertia may precipitate an anticipation of the midfoot contact, which can also explain the result observed for 5.

Plantar Pressure Measurements and Geometric Analysis of Patients With and Without Morton's Neuroma

BACKGROUND

The purpose of this research was to see if there were any differences in peak pressure, contact time, pressure-time integrals, and geometric variables such as forefoot width, foot length, coefficient of spreading, and arch index between subjects with Morton's neuroma (MN) and control subjects.

METHODS

Dynamic peak plantar pressure, contact time, pressure-time integral, and geometric data were extracted using the EMED-X platform in 52 subjects with MN and 31 control subjects. Differences in peak pressure, contact time, pressure-time integral, and geometric data between participants with and those without MN were determined using independent-samples t tests. There were no significant differences in age, weight, height, and body mass index between patients with MN and control subjects.

RESULTS

There were no significant differences in the peak pressures of all masked areas and pressure-time integrals under metatarsal 2 to 4 heads between patients with MN and control subjects. In addition, no significant differences were observed between patients with MN and control subjects in geometric measurements of forefoot length, width, coefficient of spreading, foot progression angle, and arch index.

CONCLUSION

No relationship was found in this study between peak pressure, contact time, and pressure-time integral under the metatarsal heads, forefoot width, foot length, coefficient of spreading, and foot progression angle in a symptomatic MN group compared with a control group. The need to perform osteotomies to treat MN not associated with other lesser metatarsal phalangeal joint pathologies is questionable.

LEVEL OF EVIDENCE

Level III, Case-Control Study.

An anatomically-based masking protocol for the assessment of in-shoe plantar pressure measurement of the forefoot

Background

The area beneath the metatarsal heads is a common location of foot pain, which is often associated with high plantar pressures. Current plantar pressure assessment protocols focus mainly on the gross area of the forefoot with minimal attention paid to specific areas such as the metatarsal heads. The aim of this study was to develop and assess a new anatomically-based masking protocol that is clinically relevant to measure forefoot plantar pressure during shod conditions based on the anatomical positions of the metatarsal heads.

Methods

Initially, we developed a masking protocol to measure forefoot plantar pressure during shod conditions based on the anatomical positions of the metatarsal heads. This new masking protocol divided the forefoot into three sub-areas (proximal, beneath, and distal to the metatarsal heads) as determined by the position of each metatarsal head. Following development of the new masking protocol, we compared the new protocol against a traditional protocol, which defines the forefoot as between 51 and 81% of the foot length. To compare the two masking protocols, we tested two experimental conditions: (i) a control condition (i.e. no metatarsal pad), and (ii) a metatarsal pad condition. We then compared plantar pressure differences between the two experimental conditions for the two masking protocols. Participants for this component of the study included 36 community dwelling older adults (mean age 75.6 years ±5.4) with a history of forefoot pain. Forefoot plantar pressure data were measured while walking using the pedar^®-X in-shoe system. Peak pressure, maximum force and contact area at the time of peak pressure were determined and results were compared between the two masking protocols.

Results

The traditional masking protocol showed that the metatarsal pad significantly decreased peak pressure and increased contact area in the forefoot area (i.e. within the entire mask area), but maximum force was not significantly different between the two conditions. In contrast, the newly developed anatomically-based masking protocol indicated that the metatarsal pad decreased peak plantar pressures distal to and beneath the metatarsal heads by increasing force and contact area proximal to the metatarsal heads.

Conclusions

An anatomically-based masking protocol that is clinically relevant was developed to assess forefoot plantar pressure during shod conditions based on the anatomical positions of metatarsal heads. We propose that the new forefoot masking protocol will provide greater interpretability of forefoot plantar pressure data, which will aid clinicians and researchers for diagnostic, prognostic and therapeutic purposes.

Foot Rollover Temporal Parameters During Walking Straight Ahead and Stepping Over Obstacles: Obese and Non-Obese Postmenopausal Women

The aim of this study was to compare the temporal foot rollover data between walking straight ahead and stepping over obstacles for obese and non-obese postmenopausal women. Plantar pressure data were collected from 67 women. The initial, final, and duration of contact of 10 foot areas were measured. Both limbs of both groups showed a longer foot contact duration on the obstacle task. Significant temporal differences were found for both groups between straight ahead and crossing obstacles on the initial, final, and duration of contact for several foot areas. The propulsion phase of the trailing limb during the obstacle task was anticipated. Regarding the leading limb, the first foot contact was not made with the heel areas; however, a backward foot rollover movement from the metatarsal to the heel areas occurred, possibly to provide support to better control the trailing limb swing phase.

Three-dimensional quantitative analysis of healthy foot shape: a proof of concept study

Background

Foot morphology has received increasing attention from both biomechanics researches and footwear manufacturers. Usually, the morphology of the foot is quantified by 2D footprints. However, footprint quantification ignores the foot's vertical dimension and hence, does not allow accurate quantification of complex 3D foot shape.

Methods

The shape variation of healthy 3D feet in a population of 31 adult women and 31 adult men who live in Belgium was studied using geometric morphometric methods. The effect of different factors such as sex, age, shoe size, frequency of sport activity, Body Mass Index (BMI), foot asymmetry, and foot loading on foot shape was investigated. Correlation between these factors and foot shape was examined using multivariate linear regression.

Results

The complex nature of a foot's 3D shape leads to high variability in healthy populations. After normalizing for scale, the major axes of variation in foot morphology are (in order of decreasing variance): arch height, combined ball width and inter-toe distance, global foot width, hallux bone orientation (valgus-varus), foot type (e.g. Egyptian, Greek), and midfoot width. These first six modes of variation capture 92.59% of the total shape variation. Higher BMI results in increased ankle width, Achilles tendon width, heel width and a thicker forefoot along the dorsoplantar axis. Age was found to be associated with heel width, Achilles tendon width, toe height and hallux orientation. A bigger shoe size was found to be associated with a narrow Achilles tendon, a hallux varus, a narrow heel, heel expansion along the posterior direction, and a lower arch compared to smaller shoe size. Sex was found to be associated with differences in ankle width, Achilles tendon width, and heel width. Frequency of sport activity was associated with Achilles tendon width and toe height.

Conclusion

A detailed analysis of the 3D foot shape, allowed by geometric morphometrics, provides insights in foot variations in three dimensions that can not be obtained from 2D footprints. These insights could be applied in various scientific disciplines, including orthotics and shoe design.

Analysis of modifications of the plantar parameters after the use of a proprioceptive insole: regular gait

BACKGROUND

The human foot is the basis of support and propulsion for gait and it provides support and flexibility for effective weight transfer. Therefore a correct distribution of loads and pressures on the surface of the foot is of fundamental importance to ensure a correct walking and maintaining balance in both static and dynamic conditions. The aim of our study was to evaluate the effects of innovative insoles, named regular gait (RG), on plantar pressures distribution during standing position and walking in healthy subjects; therefore, we investigated whether these effects are maintained after insole removing.

METHODS

Fifty subjects were tested; these were free of foot diseases or damage to the anatomical structures involved in the processes above posture. These subjects underwent rating scales and static and dynamic baropodometric examination before and after using RG.

RESULTS

The results obtained shown that the RG, for as we have designed, are able to change the distribution of the parameters both in static and dynamic conditions. We have also shown that the best results were obtained only after a month of treatment with RG and that the results obtained persist even in the tests post-treatment without insoles.

CONCLUSIONS

In conclusion, the data obtained in this study suggest that RG could be efficient in acting on parameters related to maintenance of upright posture and gait. This would allow the treated subjects to improve their posture both while walking and during the maintenance of the upright position.

A Pilot Study of the Effect of Outsole Hardness on Lower Limb Kinematics and Kinetics during Soccer Related Movements

The purpose of this study was to investigate the effect of different outsole hardness of turf cleats shoes on the lower limb kinematics and kinetics of soccer players playing on artificial turf. The participants were required to complete tasks of straight running and 45° left sidestep cutting movements, respectively, at the speed of 4.5 ± 0.2 m/s on artificial turf. They were asked to randomly select turf cleats shoes with a soft outsole (SO), medium hardness outsole (MO) and hard outsole (HO). During the stance phase of straight running, peak pressure and force-time integral in medial forefoot (MFF) of players wearing cleats shoes with MO were significantly higher than those wearing cleats shoes with SO. During the stance phase of a 45° cutting maneuver, players wearing cleats shoes with SO showed significantly higher peak knee flexion and abduction angles than the HO group. Players wearing cleats shoes with SO also showed higher ankle dorsiflexion and inversion angles compared with those wearing cleats shoes with HO. The vertical average loading rate (VALR) as well as peak pressure and force-time integral in the heel (H) and lateral forefoot (LFF) regions of players wearing cleats shoes with HO were significantly higher than those wearing shoes with SO. On the contrary, peak pressure and force-time integral of players wearing shoes with SO were significantly higher than those wearing shoes with HO in MFF. A higher vertical loading rate and plantar pressure of some areas may increase the potential risk of metatarsal stress fractures and plantar fasciitis. Therefore, this finding about turf cleats shoes could give some theoretic support for the design of turf cleats shoes and material optimization in the future.

Normal foot loading parameters and repeatability of the Footscan® platform system

Background

The Footscan® platform system is one of the most commonly used clinical tools for the measurement of the foot pressure. The present study was designed to assess the repeatability of the system and identify the range of loading parameters observed in the normal foot.

Methods

Measurements were collected from 32 healthy participants, 15 females and 17 males, twice at an interval of 1 week. Peak pressure (PP), contact time (CT), contact area (CA), pressure-time integral (PTI), and maximum force (MaF) were recorded; these parameters were investigated in 10 areas of the foot: medial heel, lateral heel, midfoot, first to fifth metatarsals, hallux, and toes 2–5. The intra-session repeatability was evaluated by calculating the intraclass correlation coefficients (ICCs) and coefficients of variation (CVs) across the three repeated trials within the same session. The inter-session repeatability was assessed using the average of the three trials in each session to determine the ICCs and CVs.

Results

The ICCs showed moderate to good repeatability for every variable of interest, and the CVs were all <28%. The highest zones of PP were found under the second and third metatarsals, followed by the medial heel. The CT was 68.5–82.8% of the total stance time under the metatarsal heads. CA was highest under the midfoot, PTI was highest under the second metatarsal, and MaF was highest under the medial heel.

Conclusions

Footscan® platform system was found to be repeatable. Thus, it can be used as a valuable tool in the assessment of plantar pressure distribution, and the normal values of the foot loading parameters identified in this study can be employed to provide a reference range for the gait analysis performed by the Footscan® system.

A real time study of the human equilibrium using an instrumented insole with 3 pressure sensors

The present work deals with the study of the human equilibrium using an ambulatory e-health system. One of the point on which we focus is the fall risk, when losing equilibrium control. A specific postural learning model is presented, and an ambulatory instrumented insole is developed using 3 pressures sensors per foot, in order to determine the real-time displacement and the velocity of the centre of pressure (CoP). The increase of these parameters signals a loss of physiological sensation, usually of vision or of the inner ear. The results are compared to those obtained from classical more complex systems.

A Novel Method of Quantifying Gait Deviations Using Plantar Pressure Patterns

BACKGROUND

Comparing the dynamic pedobarographic patterns of individuals is common practice in basic and applied research. However, this process is often time-consuming and complex, and commercially available software often lacks powerful visualization and interpretation tools.

METHODS

We propose a simple method for displaying pixel-level pedobarographic deviations over time relative to a so-called reference pedobarographic pattern. This novel method contains four distinct automated preprocessing stages: 1) normalization of pedobarographic fields (for foot length and width), 2) temporal normalization, 3) a pixel-level z-score-based calculation, and 4) color coding of the normalized pedobarographic fields. Group and patient-level comparisons were illustrated using an experimental data set including diabetic and nondiabetic patients.

RESULTS

The automated procedure was found to be robust and quantified distinct temporal deviations in pedobarographic fields.

CONCLUSIONS

The advantages of the novel method cover several domains, including visualization, interpretation, and education.

A color-code based method for the interpretation of plantar pressure measurements in clinical gait analysis

Comparing plantar pressure measurements (PPM) of a patient following an intervention or between a reference group and a patient-group is common practice in clinical gait analysis. However, this process is often time consuming and complex, and commercially available software often lacks powerful visualization and interpretation tools. In this paper, we propose a simple method for displaying pixel-level PPM deviations relative to a so-called reference PPM pattern. The novel method contains 3 distinct stages: (1) a normalization of pedobarographic fields (for foot length and width), (2) a pixel-level z-score based calculation and, (3) color coding of the normalized pedobarographic fields. The methodological steps associated to this novel method are precisely described and clinical output illustrated. We believe that the advantages of the novel method cover several domains. The strongest advantage of the novel method is that it provides a straightforward visual interpretation of PPM without decreasing the resolution perspective. A second advantage is that it may guide the selection of a local mapping technique (data reduction technique). Finally, it may be easily used as education tool during the therapist-patient interaction.

Foot modeling and smart plantar pressure reconstruction from three sensors

In order to monitor pressure under feet, this study presents a biomechanical model of the human foot. The main elements of the foot that induce the plantar pressure distribution are described. Then the link between the forces applied at the ankle and the distribution of the plantar pressure is established. Assumptions are made by defining the concepts of a 3D internal foot shape, which can be extracted from the plantar pressure measurements, and a uniform elastic medium, which describes the soft tissues behaviour. In a second part, we show that just 3 discrete pressure sensors per foot are enough to generate real time plantar pressure cartographies in the standing position or during walking. Finally, the generated cartographies are compared with pressure cartographies issued from the F-SCAN system. The results show 0.01 daN (2% of full scale) average error, in the standing position.