Detection of freezing of gait in Parkinson's disease from foot-pressure sensing insoles using a temporal convolutional neural network.

Backgrounds: Freezing of gait (FoG) is a common and debilitating symptom of Parkinson's disease (PD) that can lead to falls and reduced quality of life. Wearable sensors have been used to detect FoG, but current methods have limitations in accuracy and practicality. In this paper, we aimed to develop a deep learning model using pressure sensor data from wearable insoles to accurately detect FoG in PD patients. Methods: We recruited 14 PD patients and collected data from multiple trials of a standardized walking test using the Pedar insole system. We proposed temporal convolutional neural network (TCNN) and applied rigorous data filtering and selective participant inclusion criteria to ensure the integrity of the dataset. We mapped the sensor data to a structured matrix and normalized it for input into our TCNN. We used a train-test split to evaluate the performance of the model. Results: We found that TCNN model achieved the highest accuracy, precision, sensitivity, specificity, and F1 score for FoG detection compared to other models. The TCNN model also showed good performance in detecting FoG episodes, even in various types of sensor noise situations. Conclusions: We demonstrated the potential of using wearable pressure sensors and machine learning models for FoG detection in PD patients. The TCNN model showed promising results and could be used in future studies to develop a real-time FoG detection system to improve PD patients' safety and quality of life. Additionally, our noise impact analysis identifies critical sensor locations, suggesting potential for reducing sensor numbers.

Comparative study of the effects of custom-made insole and ordinary insole in adults with flexible flatfoot on different slopes.

BACKGROUND: Flatfoot (pes planus) is a common foot deformity, and its causes are mainly related to age, gender, weight, and genetics. Previous studies have shown that custom-made insoles could have a positive effect in improving plantar pressure and symptoms in individuals with flexible flatfeet, but it remains to be explored whether they can still show benefits in daily walking on different slopes. OBJECTIVE: This study aims to investigate a custom-made insole based on plantar pressure redistribution and to verify its effectiveness by gait analysis on different slopes. METHODS: We recruited 10 subjects and compared the peak pressure and impulse in each area between custom-made insole (CI) and ordinary insole (OI) groups. RESULTS: The results illustrate that CI raises the pressure in T area, improves the ability of the subjects to move forward in the slope walking, which was beneficial to gait stability. CONCLUSION: The redistribution of pressure in MF and MH area is promoted to provide active protection for subjects. Meanwhile, CI could decrease the impulse in MF area during uphill and level walking, which effectively reduces the accumulation of fatigue during gait. Moreover, avoiding downhill walking could be able to protect foot from injury in daily life.

Deep-learning enabled smart insole system aiming for multifunctional foot-healthcare applications.

Real-time foot pressure monitoring using wearable smart systems, with comprehensive foot health monitoring and analysis, can enhance quality of life and prevent foot-related diseases. However, traditional smart insole solutions that rely on basic data analysis methods of manual feature extraction are limited to real-time plantar pressure mapping and gait analysis, failing to meet the diverse needs of users for comprehensive foot healthcare. To address this, we propose a deep learning-enabled smart insole system comprising a plantar pressure sensing insole, portable circuit board, deep learning and data analysis blocks, and software interface. The capacitive sensing insole can map both static and dynamic plantar pressure with a wide range over 500 kPa and excellent sensitivity. Statistical tools are used to analyze long-term foot pressure usage data, providing indicators for early prevention of foot diseases and key data labels for deep learning algorithms to uncover insights into the relationship between plantar pressure patterns and foot issues. Additionally, a segmentation method assisted deep learning model is implemented for exercise-fatigue recognition as a proof of concept, achieving a high classification accuracy of 95%. The system also demonstrates various foot healthcare applications, including daily activity statistics, exercise injury avoidance, and diabetic foot ulcer prevention.

Effects of individually optimized rocker midsoles and self-adjusting insoles on dynamic stability in persons with diabetes mellitus and neuropathy.

BACKGROUND: Persons with diabetic peripheral neuropathy (DPN) may face challenges such as balance issues due to reduced somatosensory feedback and an increased risk of developing diabetic foot ulcers (DFUs) due to increased plantar pressure. Pressure reducing footwear is thought to further impair balance. We introduced 3D-printed rocker midsoles and self-adjusting insoles that are able to reduce elevated plantar pressure values and aimed to prevent balance deterioration. However, their effect on the balance during walking (dynamic stability) is not analyzed yet. RESEARCH QUESTION: Is dynamic stability of persons with DPN impaired compared to healthy individuals and what is the effect of the 3D-printed rocker midsoles and self-adjusting insoles on the dynamic stability in this population? METHODS: Dynamic stability, specifically the margins of stability (MOS) in the anterior-posterior (AP) and medio-lateral (ML) direction, was measured in ten healthy and nineteen persons with DPN. Independent-samples t-test was applied to analyze the difference in the MOS between groups. One-way repeated measures analyses of variance (ANOVA) was conducted to test the difference between the therapeutic footwear combinations within the DPN group. RESULTS: There is no significant difference between the healthy and DPN group in MOS-AP. MOS-ML is significantly larger in DPN compared to the healthy participants. Using the self-adjusting insole shows a significantly lower (negative) MOS-AP compared to when using a rocker shoe within the DPN group. SIGNIFICANCE: This study provides valuable information on whether DPN and our therapeutic footwear have a negative effect on the dynamic stability. DPN does not have a negative effect on dynamic stability in the AP direction. For the ML direction, DPN seems to cause larger MOS-ML by likely using a compensation strategy (e.g., wider steps) while our experimental footwear does not further impair the MOS-ML.

Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

Forensic measurement comparison of foot insole impressions.

In forensic podiatry, footprints have been shown to provide a valuable source of discriminatory information. Footprints may be found in various forms, such as bare footprints, sock-clad footprints, or as impressions on insoles within footwear. This study utilized quantitative measures of foot impressions on pairs of insoles from shoes worn by the same person from a population of 31 adults. The measurements were determined by using the Reel method and comprised measurements from the heel to the tips of the toes and width of the ball. The purpose of the study was to assess the margin of error for these measurements to determine whether they were sufficiently accurate for forensic use. A secondary purpose of this study was to determine whether the analyst's experience or lack thereof in forensic podiatry had an impact on the precision of measurement data. The insole foot impressions were assessed by two podiatrists with forensic podiatry experience in footprint analysis, footprint research, and in using the Reel method of footprint measurement, as well as by three students of podiatric medicine without any such experience. A statistical analysis of the data from the study was performed using SPSS v28 (IBM SPSS Statistics for Windows, Version 28.0. Armonk, NY: IBM Corp). The most reliable measurements were of forefoot width, heel to first toe, heel to second toe, and heel to fourth toe. The greatest variation occurred in the measurements of the heel to the third and fifth toes. The measurements of the forensic podiatrist analysts showed less variability than those of the podiatry students, suggesting that measurement precision is related to the experience of the analyst.

Role of insole material in treatment of plantar fasciitis: A randomized clinical trial.

BACKGROUND: In this randomized clinical trial, we compared the early effects of polyethylene (PE), polyurethane (PU), and Carbon Fiber insoles in the treatment of PF using a set of patient-reported outcomes. METHODS: Patients were randomly allocated one of the three prefabricated insoles - Carbon Fiber (n = 14), PU (n = 14), or PE (n = 17) for regular use. Their response was recorded using PROMIS 3a (for pain intensity), PROMIS 4a (for pain interference), FAOS (Foot and Ankle Outcome Score), and VAS for pain at baseline, two, six, and twelve weeks. RESULTS: The PROMIS pain intensity scores improved in both the Carbon Fiber and the PE groups starting at the 6th week (p = 0.04) and 2nd week (p = 0.002), respectively. PROMIS pain interference scores also showed positive trends in these two groups (p = 0.02, p = 0.004, respectively). CONCLUSION: Prefabricated Carbon Fiber and PE insoles showed significant pain-reducing effects in patients with PF. LEVELS OF EVIDENCE: Level I, Randomized controlled trial.

Smart wearable insoles in industrial environments: A systematic review.

BACKGROUND AND PURPOSE: Industrial environments present unique challenges in ensuring worker safety and optimizing productivity. The emergence of smart wearable technologies such as smart insoles has provided new opportunities to address these challenges through accurate unobtrusive monitoring and analysis of workers' activities and physical parameters. This systematic review aims to analyze the utilization of smart wearable insoles in industrial environments, focusing on their applications, employed analysis methods, and potential future directions. METHODS: A comprehensive review was conducted, involving the analysis of 27 papers that utilized smart wearable insoles in industrial settings. The reviewed articles were evaluated to determine the trends in application and methodology, explore the implementation of smart insoles across different industries, and identify the prevalent machine learning models and analyzed activities in the relevant literature. RESULTS: The majority of the reviewed articles (67%) primarily focused on human activity recognition and gesture estimation using smart wearable insoles, aiming to enhance safety and productivity in industrial settings. Furthermore, 10% of the studies focused on fatigue identification, 10% on slip, trip, and fall hazard detection, and 13% on biomechanical analyses of workers' body joint loads. The construction industry accounted for approximately 60% of the studies conducted in industrial settings using smart insoles. The most prevalent machine learning models utilized in these studies were neural networks (48%), support vector machines (33%), k-nearest neighbors (30%), decision trees (26%), and random forests (15%). These models achieved median accuracies of 95%, 96%, 91%, 92%, and 95%, respectively. Among the analyzed activities, walking, bending with/without lifting/lowering a load, and carrying a load were the most frequently considered, with frequencies of 10, 10, and 7 out of the 27 studies, respectively. CONCLUSION: The findings of this systematic review demonstrate the growing interest in implementing smart wearable insoles in industrial environments to enhance safety and productivity. However, the effectiveness of these systems is dependent on factors such as accuracy, reliability, and generalizability of the models. The review highlights the need for further research to address these challenges and to explore the potential of these systems for use in other industrial applications such as manufacturing. Overall, this systematic review provides valuable insights for researchers, practitioners, and policymakers in the field of occupational health and safety.

Hallux Valgus Plantar Pressure Distribution before and after a Distal Metatarsal Osteotomy.

Background: Hallux valgus (HV) morphological alterations impact forefoot kinetics. Surgery aims to restore both the morphology and function. Plantar pressure (PP) distribution systems represent an innovative additional tool to evaluate the hallux functional outcome after surgery in order to assess the hallux dorsiflexion, coupled with plantar flexion of the first ray. However, the literature reports limited evidence regarding the rebalancing of the plantar pressure distribution following surgery. The purpose of the present study was to examine the PP distribution in HV patients before and after a distal metatarsal osteotomy using a novel anatomically based protocol for in-shoe plantar load analysis during gait. Methods: A consecutive series of 18 patients with mild-to-moderate symptomatic HV who underwent a distal metatarsal osteotomy (S.E.R.I. technique) were prospectively evaluated using clinical scores (AOFAS and NRS), radiographic parameters (hallux valgus angle, intermetatarsal angle), and PP measurements via W-INSHOE© (Medicapteurs, Balma, France). Data were collected preoperatively and 12 months after surgery. Results: At 12 months follow-up, 3 patients were lost to follow-up, leaving 15 patients (24 HV) for examination. Both clinical and radiographical outcomes showed significant improvements from the pre- to postoperative periods. The PP distribution pattern revealed a significant increase in the peak pressure under the first metatarsal head associated with a significant increase in the peak pressure under the central metatarsals area between the pre- and postoperative periods. Conclusions: PP measurement systems hold promise as an additional clinical tool, yet current findings remain inconclusive. Further long-term follow-up studies that incorporate additional parameters are warranted.

In-silico techniques to inform and improve the personalized prescription of shoe insoles.

Background: Corrective shoe insoles are prescribed for a range of foot deformities and are typically designed based on a subjective assessment limiting personalization and potentially leading to sub optimal treatment outcomes. The incorporation of in silico techniques in the design and customization of insoles may improve personalized correction and hence insole efficiency. Methods: We developed an in silico workflow for insole design and customization using a combination of measured motion capture, inverse musculoskeletal modelling as well as forward simulation approaches to predict the kinematic response to specific insole designs. The developed workflow was tested on twenty-seven participants containing a combination of healthy participants (7) and patients with flatfoot deformity (20). Results: Average error between measured and simulated kinematics were 4.7 ± 3.1, 4.5 ± 3.1, 2.3 ± 2.3, and 2.3 ± 2.7° for the chopart obliquity, chopart anterior-posterior axis, tarsometatarsal first ray, and tarsometatarsal fifth ray joints respectively. Discussion: The developed workflow offers distinct advantages to previous modeling workflows such as speed of use, use of more accessible data, use of only open-source software, and is highly automated. It provides a solid basis for future work on improving predictive accuracy by adapting the currently implemented insole model and incorporating additional data such as plantar pressure.

Effects of foot orthoses on running kinetics and kinematics: A systematic review and meta-analysis.

BACKGROUND: Foot orthoses (FOs) are often prescribed by clinicians to treat foot and ankle conditions, prevent running injuries, and enhance performance. However, the lack of higher-order synthesis of clinical trials makes it challenging for clinicians to adopt an evidence-based approach to FOs' prescriptions. RESEARCH QUESTION: Do FOs with different modifications alter lower extremity running kinematics and kinetics? METHODS: A systematic search of seven databases was conducted from inception to February 2023. The analysis was restricted to healthy adults without foot musculoskeletal impairments and studies that compared the FOs effects with the controls. The methodological quality of the 35 studies that met the eligibility criteria was evaluated using the modified Downs and Black checklist. The random effects model estimated the standardized mean difference (SMD) with 95% confidence intervals and effect sizes. Sub-group analyses based on FOs type were performed to assess the potential effects of the intervention. RESULTS: Our findings indicated that both custom and off-the-shelf arch-support FOs reduced peak plantar pressure at the medial heel (SMD=-0.35, and SMD=-1.03), lateral heel (SMD=-0.50, and SMD=-0.53), and medial forefoot (SMD=-0.20, and SMD=-0.27), but increased plantar pressure at the mid-foot (SMD=0.30, and SMD=0.56). Compared with the controls, significant increases (SMD=0.36) in perceived comfort were found with custom FOs. A reduction (SMD=-0.58) in initial ankle inversion was found when a raised heel cup was integrated with arch-support FOs. A medial post integrated with arch support exhibited a reduced ankle (SMD=-1.66) and tibial (SMD=-0.63) range of motion. Custom FOs, however, unfavorably affected the running economy (SMD=-0.25) and perceived exertion (SMD=0.20). SIGNIFICANCE: Although FOs have been reported to have some positive biomechanical effects in healthy populations without musculoskeletal impairments or running-related issues, they need to be optimized and generalized to achieve better running performance and prevent injury.

Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy.

Ground reaction force (GRF) components can be estimated using insole pressure sensors. Principal component analysis in conjunction with machine learning (PCA-ML) methods are widely used for this task. PCA reduces dimensionality and requires pre-normalization. In this paper, we evaluated the impact of twelve pre-normalization methods using three PCA-ML methods on the accuracy of GRF component estimation. Accuracy was assessed using laboratory data from gold-standard force plate measurements. Data were collected from nine subjects during slow- and normal-speed walking activities. We tested the ANN (artificial neural network) and LS (least square) methods while also exploring support vector regression (SVR), a method not previously examined in the literature, to the best of our knowledge. In the context of our work, our results suggest that the same normalization method can produce the worst or the best accuracy results, depending on the ML method. For example, the body weight normalization method yields good results for PCA-ANN but the worst performance for PCA-SVR. For PCA-ANN and PCA-LS, the vector standardization normalization method is recommended. For PCA-SVR, the mean method is recommended. The final message is not to define a normalization method a priori independently of the ML method.

Exploring the Potential of Lateral Wedge Insoles in Alleviating Bone Marrow Lesions in End-Stage Knee Osteoarthritis: A Preliminary Case Report.

The efficacy of lateral wedge insoles (LWIs) in patients with end-stage knee osteoarthritis (OA) is unclear. A 43-year-old male underwent two anterior cruciate ligament reconstructions in his right knee and was later diagnosed with end-stage knee OA. An LWI combining arch support with a lateral heel wedge was fabricated for this patient and used over 12 months. As a result, after 12 months, the bone marrow lesion (BML), as measured by the magnetic resonance imaging Osteoarthritis Knee Score (MOAKS), was downgraded from grade 2 to grade 1. The use of LWI in a patient with end-stage knee OA showed lower co-contraction ratios in knee muscles even after 12 months. The results provide preliminary evidence suggesting the use of LWI in patients with end-stage knee OA has potential benefits for reducing BML.

Differences in kinematic parameters during gait between the patients with knee osteoarthritis and healthy controls using an insole with a single inertial measurement unit: A case-control study.

BACKGROUND: An inertial measurement unit is small and lightweight, allowing patient measurements without physical constraints. This study aimed to determine the differences in kinematic parameters during gait using an insole with a single inertial measurement unit in healthy controls and on both sides in patients with knee osteoarthritis. METHODS: Twenty patients with knee osteoarthritis and 13 age-matched controls were included in this study. The participants walked at a self-selected speed and foot kinematics were measured during gait using an insole with a single inertial measurement unit. The right side of the healthy controls and both the affected and contralateral sides of patients with KOA were analyzed separately. FINDINGS: The foot extension angular velocity at toe-off was significantly reduced on the affected side than on the contralateral side (P < 0.001) and in healthy controls (P < 0.001). During the swing phase, foot posterior-anterior acceleration was significantly lower on the affected side than on the healthy controls (P = 0.005). Furthermore, despite a decrease in walking speed, foot superior-inferior acceleration at initial contact in patients was significantly lower on the contralateral side than in healthy controls (P = 0.0167), but not on the affected side (P = 0.344). INTERPRETATION: An insole with a single inertial measurement unit can detect differences in foot kinematics during gait between healthy controls and patients with knee osteoarthritis. Our findings indicate that patients with knee osteoarthritis exhibit dysfunction of push-off at toe-off and shock absorption at initial contact on the affected side.

Capacitive Pressure Sensor Combining Dual Dielectric Layers with Integrated Composite Electrode for Wearable Healthcare Monitoring.

Foot activity can reflect numerous physiological abnormalities in the human body, making gait a valuable metric in health monitoring. Research on flexible sensors for gait monitoring has focused on high sensitivity, wide working range, fast response, and low detection limit, but challenges remain in areas such as elasticity, antibacterial activity, user-friendliness, and long-term stability. In this study, we have developed a novel capacitive pressure sensor that offers an ultralow detection limit of 1 Pa, wide detection ranges from 1 Pa to 2 MPa, a high sensitivity of 0.091 kPa-1, a fast response time of 71 ms, and exceptional stability over 6000 cycles. This sensor not only has the ability of accurately discriminating mechanical stimuli but also meets the requirements of elasticity, antibacterial activity, wearable comfort, and long-term stability for gait monitoring. The fabrication method of a dual dielectric layer and integrated composite electrode is simple, cost-effective, stable, and amenable to mass production. Thereinto, the introduction of a dual dielectric layer, based on an optimized electrospinning network and micropillar array, has significantly improved the sensitivity, detection range, elasticity, and antibacterial performance of the sensor. The integrated flexible electrodes are made by template method using composite materials of carbon nanotubes (CNTs), two-dimensional titanium carbide Ti3C2Tx (MXene), and polydimethylsiloxane (PDMS), offering synergistic advantages in terms of conductivity, stability, sensitivity, and practicality. Additionally, we designed a smart insole that integrates the as-prepared sensors with a miniature instrument as a wearable platform for gait monitoring and disease warning. The developed sensor and wearable platform offer a cutting-edge solution for monitoring human activity and detecting diseases in a noninvasive manner, paving the way for future wearable devices and personalized healthcare technologies.

Development of Cyclic Pressure Offloading Insole for Diabetic Foot Ulcer Prevention.

Introduction. The likelihood of developing a diabetic foot ulcer (DFU) during one's lifetime for individuals with diabetes mellitus is around 19% to 34%. Continuous and repetitive loading on soft tissues are the major causative factors for DFU. This paper introduces an air cell array insole designed for cyclically offloading pressure from plantar regions to reduce repetitive stress and loading on foot. Materials and Methods. The insole comprises an air cell array insole and a pneumatic control unit. The interface pressure was evaluated in static and dynamic conditions at 3 different air cell internal pressures (6.9, 10.3, and 13.8 kPa). Plantar interface pressure was measured using a commercial pressure system, and data were analyzed using paired t test. Average interface pressure and peak pressure (PP) were studied to evaluate the functionality and effectiveness of the insole. Results. The analysis of static pressure data revealed that cyclic offloading significantly (p < .05) reduced PP in 4 tested cells corresponding to big toe, metatarsal heads, and heel areas with the maximum mean difference of 12.9 kPa observed in big toe region. Similarly, dynamic pressure data analysis showed that cyclic offloading significantly (p < .05) reduced PP in these areas, with the highest mean PP reduction of 36.98 kPa in the big toe region. Discussion. Results show the insole's capability to reduce plantar pressure through cyclic offloading. Internal pressure of air cells significantly affects the overall pressure reduction and must be chosen based on the user's weight. Conclusion. Results confirm that the insole with offloading capabilities has the potential to reduce the risk of developing DFUs by alleviating the plantar stress during both static and dynamic conditions.

Insole Pressure Sensors to Assess Post-Stroke Gait.

OBJECTIVE: To confirm that the simplified insole does not affect the gait speed and to identify objective sensor-based gait parameters that correlate strongly with existing clinical gait assessment scales. METHODS: Ten participants with gait impairment due to hemiplegic stroke were enrolled in this study. Pairs of insoles with four pressure sensors on each side were manufactured and placed in each shoe. Data were extracted during the 10-Meter Walk Test. Several sensor-derived parameters (for example stance time, heel_on-to-toe_peak time, and toe_peak pressure) were calculated and correlated with gait speed and lower extremity Fugl-Meyer (F-M) score. RESULTS: The insole pressure sensor did not affect gait, as indicated by a strong correlation (ρ=0.988) and high agreement (ICC=0.924) between the gait speeds with and without the insole. The parameters that correlated most strongly with highest ß coefficients against the clinical measures were stance time of the non-hemiplegic leg (ß=-0.87 with F-M and ß=-0.95 with gait speed) and heel_on-to-toe_peak time of the non-hemiplegic leg (ß=-0.86 with F-M and -0.94 with gait speed). CONCLUSION: Stance time of the non-hemiparetic leg correlates most strongly with clinical measures and can be assessed using a non-obtrusive insole pressure sensor that does not affect gait function. These results suggest that an insole pressure sensor, which is applicable in a home environment, may be useful as a clinical endpoint in post-stroke gait therapy trials.

Effects of gait modifications on tissue-level knee mechanics in individuals with medial tibiofemoral osteoarthritis: A proof-of-concept study towards personalized interventions.

Gait modification is a common nonsurgical approach to alter the mediolateral distribution of knee contact forces, intending to decelerate or postpone the progression of mechanically induced knee osteoarthritis (KOA). Nevertheless, the success rate of these approaches is controversial, with no studies conducted to assess alterations in tissue-level knee mechanics governing cartilage degradation response in KOA patients undertaking gait modifications. Thus, here we investigated the effect of different conventional gait conditions and modifications on tissue-level knee mechanics previously suggested as indicators of collagen network damage, cell death, and loss of proteoglycans in knee cartilage. Five participants with medial KOA were recruited and musculoskeletal finite element analyses were conducted to estimate subject-specific tissue mechanics of knee cartilages during two gait conditions (i.e., barefoot and shod) and six gait modifications (i.e., 0°, 5°, and 10° lateral wedge insoles, toe-in, toe-out, and wide stance). Based on our results, the optimal gait modification varied across the participants. Overall, toe-in, toe-out, and wide stance showed the greatest reduction in tissue mechanics within medial tibial and femoral cartilages. Gait modifications could effectually alter maximum principal stress (~20 ± 7%) and shear strain (~9 ± 4%) within the medial tibial cartilage. Nevertheless, lateral wedge insoles did not reduce joint- and tissue-level mechanics considerably. Significance: This proof-of-concept study emphasizes the importance of the personalized design of gait modifications to account for biomechanical risk factors associated with cartilage degradation.

Case study: Gait assessment of a patient with hallux rigidus before and after plantar modification.

INTRODUCTION AND IMPORTANCE: Hallux rigidus (HR) is a degenerative arthritis affecting the first metatarsophalangeal joint (MTP), leading to pain and functional impairment, particularly during the propulsive phase of walking. The prevalence of HR is about 2.5 % in individuals over 50, but younger individuals can also be affected, as demonstrated in this case. CASE PRESENTATION: We report the case of a 26-year-old patient with a body mass index (BMI) of 20.2, who has been suffering from HR for 5 years. The patient presented with walking difficulties, characterized by a limp and impaired propulsion phase, and pain in the right foot due to HR. A comprehensive gait assessment was conducted using a baropodometric platform and integrated smartphone motion sensors. Following the diagnosis, a non-surgical intervention involving the application of a compressed cotton felt foot orthosis at the MTP plantar area was initiated. This intervention aimed to alleviate pain and improve the functional mobility of the right big toe. Post-treatment assessments showed an increase in the big toe's mobility from 0 degrees to 35 degrees, as measured by a digital goniometer. CLINICAL DISCUSSION: The application of a soft support, such as compressed cotton felt, at the plantar area of MTP, demonstrated a potential non-surgical therapeutic approach to improve gait and reduce discomfort in HR patients. CONCLUSION: This case study underscores the potential benefits of plantar modification in the management of HR.