Search Results (312)

Peripheral neuropathy degrades gait mechanics, elevating peak plantar pressures (PPP) and tissue ulceration risks. This exploratory pilot study evaluates custom orthotics using thermo-mechanically synthesized re-entrant auxetic foam insoles with targeted dome-shaped inserts. Bilateral dynamic gait analysis was conducted across a heterogeneous cohort (N = 9) utilizing a P-Walk 600 pressure plate and a MARVUE 2D motion capture system inside standardized footwear. To address small-sample limits, a non-parametric bootstrap resampling analysis (B = 1000) was executed. Native auxetic foam demonstrated high internal consistency ((σ*) = 12.063 kPa, Consistency Rank = 1), showing a strong numerical propensity to restrict load distribution variability compared to factory-installed over-the-counter (OTC) memory foam controls. While initial cell-adaptation cycles were observed, custom insoles reduced PPP by up to 62.2% in systemic polyneuropathies, shifting signatures beneath the exploratory clinical safety target (<200 kPa) in 55% of simulated cases. Conversely, customization triggered an adverse volumetric crowding effect in focal mononeuropathies, suggesting un-customized native auxetic foam as the preferred structural configuration for this specific cohort to avoid premature cell densification. Re-entrant cellular structures accommodate heterogeneous pathomechanics, offering an exploratory pathway for patient-specific orthotic optimization. Full article

Footwear geometry plays an important role in determining mechanical performance, plantar load distribution, comfort, and foot stability during gait. Understanding how constructive design parameters influence foot biomechanics is essential for developing footwear that improves comfort while reducing excessive mechanical loading. Although various finite element studies have investigated insole and outsole design, midsole materials, and plantar pressure redistribution, comparatively little attention has been paid to the influence of upper construction parameters, particularly vamp opening configuration, on the biomechanical behaviour of feet and footwear. This study investigates how the opening amplitude of the vamp affects the biomechanical response of three constructive variants—medium (M1), wide (M2), and narrow (M3) vamp openings—developed on a common shoe last derived from anthropometric data. Finite element analysis was conducted using ANSYS 17.2, with 3D models built in Delcam Crispin ShoeMaker Pro 2015 R2 for the three loading scenarios. Total deformation and von Mises stress were extracted as primary output parameters for both the foot and footwear. The results indicate that wider vamp openings increase structural flexibility, with M2 recording the highest total deformation across multiple scenarios, whereas narrower openings generate elevated stress concentrations, particularly in loading scenario 2. The medium vamp opening (M1) demonstrated the most favourable stress distribution overall, with a maximum stress of 1.838 Megapascals (MPa). Validation against experimental plantar pressure data confirmed that loading scenario 3 follows the same plantar pressure distribution trend as in the biomechanical study. The results confirm finite element analysis as an effective computational tool for footwear design evaluation and indicate that vamp amplitude should be considered alongside material selection and geometry as a key variable influencing comfort, fit, and structural performance. Full article

Parkinson’s disease (PD) is associated with gait impairment, bilateral asymmetry, and increased gait variability, highlighting the need for objective and interpretable wearable gait assessment. Plantar insole recordings directly capture foot–ground loading, but their use in PD assessment is often limited by global or low-order descriptors that do not fully represent regional loading organization. This study proposes a region-specific information-theoretic framework for PD gait assessment using wearable plantar-pressure insoles. Bilateral plantar insole signals were reorganized into five anatomical regions: heel, rearfoot, midfoot, forefoot, and toe. Self-information index (SII), Shannon entropy (EN), negentropy (NEG), sample entropy (SEN), and Kullback–Leibler divergence (KL) features were extracted to characterize self-information fluctuation, probabilistic uncertainty, non-Gaussian organization, temporal irregularity, and directional distributional discrepancy in plantar-pressure dynamics. The resulting feature representation was evaluated at gait-cycle, walking-recording, and subject-independent levels using conventional classifiers, ablation analysis, subject-balanced cycle aggregation, and an information-theoretic three-dimensional feature-space rule model (ITFS-RM). KNN achieved an accuracy of 0.9668 at the gait-cycle level, and MLP achieved an accuracy of 0.9344 at the walking-recording level. Under stricter subject-independent evaluation, the accuracy was 0.8475, and subject-balanced-cycle aggregation achieved an accuracy of 0.8655. Region-specific analysis and ablation experiments showed spatially heterogeneous HC–PD differences, with the toe region showing the most consistent contribution. SII, KL, and NEG provided stable discriminative contributions, particularly in toe-related and regional-transition features. ITFS-RM provided explicit feature combinations, value ranges, and spatial rule boundaries for interpretable walking-recording level and subject-grouped separation. These results support region-specific information-theoretic analysis as an interpretable representation of plantar-pressure dynamics for PD gait assessment and emphasize the need for subject-wise validation when repeated walking recordings are available. Full article

Accurate plantar pressure distribution is important for biomechanics, gait analysis, rehabilitation, and diabetic foot assessment. However, wearable plantar pressure systems are often limited by sparse sensor layouts due to hardware complexity, power consumption, and user comfort constraints. This study proposes a multimodal deep learning framework for static plantar pressure prediction using plantar geometry information and sparse landmark constraints. A convolutional block attention module U-Net architecture was developed to integrate plantar geometry and sparse landmark modalities through dual-encoder feature fusion with attention refinement. Different network architectures, fusion strategies, and landmark densities were systematically evaluated using a controlled-variable experimental design. Results demonstrated that feature-level fusion consistently outperformed data-level fusion and unimodal configurations across all landmark densities. The proposed model achieved the best performance with a normalized root mean square error of 0.087 using 16 landmarks, and the same model maintained a normalized root mean square error of 0.138 using only two landmarks, indicating promising reconstruction performance even under highly sparse sensing conditions. Marginal contribution and synergy analyses further showed that feature-level fusion more effectively captured complementary interactions between plantar geometry and sparse anatomical guidance, particularly under sparse landmark conditions. These findings suggest that multimodal feature-level fusion provides an effective strategy for sparse-to-dense plantar pressure reconstruction and may support the development of low-cost intelligent insole systems for biomechanical monitoring and clinical applications. Full article

Background: The management of diabetic foot disease and knee osteoarthritis (OA) with smart orthotics holds significant importance during the early stages of these conditions, given their potential consequences, including functional impairment, chronic pain, and economic burden. Real-time monitoring of plantar foot pressure enables early detection of abnormal force distribution and gait biomechanics, allowing for the redirection of forces away from affected ulcers or arthritic joints. This is the first systematic review to synthesise clinical evidence for smart orthotics technology with real-time plantar pressure sensor biofeedback across both diabetic foot ulcer prevention and knee osteoarthritis management simultaneously. A search of the PROSPERO register confirmed no existing registration covers this specific combination. Objectives: To examine the clinical evidence for the use of standard and smart orthotics in the prevention and management of diabetic foot ulcers (DFUs) and knee OA, and to evaluate their impact on plantar pressure redistribution, ulcer recurrence, pain, biomechanics, and economic burden. Eligibility criteria: Studies published in English involving human adult participants (≥18 years) with a clinical diagnosis of diabetes mellitus (at risk of DFU or with peripheral neuropathy) or knee OA, where the intervention involved any orthotic device or smart/intelligent insole with clinical outcomes reported, were included. Studies on healthy individuals only, those not reporting participant age, and non-weight-bearing protocols not differentiated from weight-bearing were excluded. Information sources: Five databases were searched: CINAHL (EBSCO Information Services, Ipswich, MA, USA), PubMed Advanced (National Library of Medicine, Bethesda, MD, USA), Wiley Online Library (John Wiley & Sons, Hoboken, NJ, USA), Cochrane Library (Cochrane Collaboration, London, UK), and Google Scholar (Google LLC, Mountain View, CA, USA). Searches were completed in May 2026. Methods: We conducted a comprehensive literature review. This review was structured and reported with reference to the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis; University of Ottawa, Ottawa, ON, Canada) to guide transparency of reporting. It does not constitute a full Cochrane-style systematic review; risk of bias assessment was applied to key included studies and GRADE (Grading of Recommendations Assessment, Development and Evaluation; McMaster University, Hamilton, ON, Canada) certainty ratings were applied informally and narratively rather than as formal per-outcome evidence profiles. Five databases were searched yielding 92,637 records. After removal of 398 duplicates by Rayyan, 92,239 records remained. A subsequent automated keyword-based relevance filter applied within Rayyan (Rayyan AI, Doha, Qatar), prior to human screening, excluded 84,572 records that did not contain any terms related to orthotics, diabetic foot, or knee osteoarthritis, yielding 7667 records for human title/abstract screening. A narrative synthesis approach was adopted owing to the heterogeneity of study designs and outcome measures across included studies, which precluded meta-analysis. This review was not prospectively registered. A complete list of all 78 included studies, including those not individually discussed in the results and discussion. Results: The available clinical studies report promising findings for orthotics and smart orthotics in pain reduction, ulcer prevention, and potential reduction in economic burden, though conclusions are limited by small sample sizes, heterogeneity, and predominantly open-label designs. Recent research found that orthotics can be used to alter the gait pattern that influences knee OA by reducing excessive force on the affected joint. A randomised controlled trial demonstrated an 80% relative risk reduction in DFU recurrence (RR = 0.20; 95% CI: 0.06–0.79; p = 0.022), with absolute event rates of 6.3% in the intervention group versus 30.8% in controls (ARR = 24.5%); a second trial reported a 71% reduction in ulcer incidence over 18 months; and a third randomised controlled trial demonstrated statistically significant plantar pressure reduction (p < 0.01) in patients with diabetic neuropathy. Conclusions: The available evidence suggests that orthotics may be associated with improved pressure redistribution, reduced ulcer incidence, and benefit in the management of knee OA. Although the number of studies directly comparing smart orthotics with standard orthotics remains limited, the limited comparative studies suggested that smart orthotics showed promising results in reducing ulcer incidence, providing the patient with real-time feedback to offload via their electronic devices. These findings, while preliminary, highlight the potential of smart orthotic technology as an adjunct to standard orthotic care in reducing the overall burden of diabetic foot disease and knee osteoarthritis. Limitations: The primary methodological limitation of this review is the open-label design of all included smart orthotic trials, which precludes participant blinding and introduces performance bias. However, this limitation is structural and inherent to the wearable technology field—analogous to surgical trials—and is substantially mitigated by the use of objective primary outcome measures (plantar pressure and ulcer recurrence) across the three included RCTs, the consistency of effect direction across independent RCTs conducted in different countries, and a narrative sensitivity analysis confirming robustness of findings (Risk of Bias Across Studies Section). Formal per-outcome GRADE evidence profiles were not produced; overall certainty of evidence was assessed narratively with reference to GRADE domains and is judged to be low to moderate for smart orthotics in DFU prevention and low for knee OA management, consistent with the Level 2–3 evidence base and open-label study designs. Future adequately powered, multi-site RCTs with standardised outcome reporting, minimum 24-month follow-up, and integrated health economic modelling are the highest priority to extend these preliminary findings. Registration: This review was not prospectively registered. Full article

Anterior cruciate ligament (ACL) injuries remain a leading cause of morbidity in athletic populations, with 70–80% occurring through non-contact mechanisms driven by biomechanical risk factors including knee valgus (>10°), low knee flexion (<30°), tibial internal rotation (>20°), and loading asymmetry (>15°), yet implementation of evidence-based neuromuscular training (which reduces injury risk by 50–70%) remains limited due to barriers in identifying at-risk individuals through accessible field-based screening. This narrative review synthesizes motion analysis technologies spanning laboratory-based optical systems (marker-based), wearable inertial measurement units (IMUs), computer vision and marker-less pose estimation, force plate and pressure-sensitive insole systems, and integrated drone-based field assessment platforms to address this critical gap. We present a three-tier clinical screening framework that progresses from basic anthropometric and single-plane video analysis to multi-modal biomechanical assessment using real-time kinematic feedback. As an illustrative example of emerging field-deployable technology, an integrated drone-based motion capture and smart insole system combining 4K video capture, AI-driven 3D motion reconstruction, and plantar pressure mapping is described to demonstrate how laboratory-quality biomechanical assessment can be achieved in ecologically valid field settings. This evidence-based review addresses current gaps between laboratory research and practical field deployment, with emphasis on cost-effectiveness, accessibility, and clinical utility for ACL injury prevention in diverse sporting environments. Full article

Diabetic foot ulcers represent a severe complication of diabetes mellitus, affecting millions of adults worldwide and often leading to hospitalization and amputation. Diabetic neuropathy increases the risk of plantar injuries, while the lack of continuous monitoring and delayed detection contributes to the progression of these lesions. This study presents a low-cost smart insole system for continuous plantar pressure monitoring and screening of plantar pressure patterns associated with diabetic neuropathy. The system integrates piezoresistive sensors distributed across key regions of the foot, connected to a low-power ESP32 microcontroller for data acquisition. Measurements are transmitted via Bluetooth Low Energy to a mobile application that enables real-time visualization, user management, and storage in a MySQL database for historical data consultation. Data processing employs a convolutional neural network configured to classify plantar pressure patterns between non-diabetic individuals and diabetic patients presenting neuropathic alterations. System validation demonstrated 88% accuracy, 88% recall, and 87% F1-score in classifying plantar pressure patterns. The results confirm that the combination of low-cost hardware and open-source software constitutes a viable and scalable solution for screening biomechanical alterations associated with diabetic foot complications. Full article

Accurate detection of initial contact (IC) during side-cutting is essential for evaluating m. semitendinosus (ST) pre-activity, a protective mechanism against ACL injury in team sport athletes. This study developed two insole pressure sensor (IPS) algorithms—a body weight-based and a criteria-based algorithm—for IC detection and evaluated their agreement with force-plate-derived IC based on vertical ground reaction forces (vGRF). Twenty-six adult female athletes performed sport-specific side-cutting while IPS, vGRF, and ST electromyography were recorded. IPS-derived IC events were compared with vGRF-derived IC, and ST pre-activity within 50 ms prior to IC was compared between methods. Agreement and limits of agreement (LoA) were evaluated using Bland–Altman analysis. The body weight-based IPS algorithm showed a systematic delay in IC detection of 9.2 ms (LoA: 4.1 to 14.3 ms) and a −3.5 percentage point bias in ST pre-activity (LoA: −8.9 to 1.9% of MVC). In contrast, the criteria-based IPS algorithm, demonstrated minimal bias in IC detection (−0.1 ms; LoA: −3.5 to 3.4 ms) and ST pre-activity (−0.1% MVC; LoA: −1.9 to 1.7% of MVC). These findings suggest the criteria-based IPS algorithm enables accurate IC detection, supporting its potential for practical monitoring of ST pre-activity during sports-specific side-cutting outside laboratory environments. Full article

Objectives: To systematically synthesize existing evidence on functional biomechanical tests of the foot and ankle in physiotherapy and sports, focusing on their outcome measures, compatibility with wearable sensor technologies, and psychometric properties. Methods: We performed a systematic review (PRISMA-guided) of PubMed, Web of Science, PEDro, and SPORTDiscus from inception to December 2025. Eligible studies evaluated functional foot/ankle biomechanics in athletes, healthy adults, or adults with musculoskeletal foot/ankle conditions using wearable sensors (e.g., IMUs, wireless pressure insoles). Two reviewers independently screened, extracted data, and appraised methodological quality using the COSMIN Risk of Bias tool, applying property-specific ratings. Heterogeneity precluded meta-analysis; findings were narratively synthesized and tabulated. Results: Twenty full texts were reviewed; four studies (n = 83 participants) met the inclusion criteria. Wearable devices included foot- or trunk-mounted IMUs and wireless pressure insoles. Reported outcomes spanned temporal gait events and inner-stance phases, vertical ground reaction force (vGRF) and centre-of-pressure trajectories, running step rate/stride length, and jump counts in competition. Validity was most frequently assessed: foot-worn IMUs showed millisecond-level agreement with in-shoe pressure references for stance and inner-stance events; pressure insoles demonstrated acceptable agreement with force plates for vGRF/COP alongside fair-to-excellent test–retest reliability; foot- vs. shank-mounted IMUs provided strong agreement for running step rate and stride length; and competition-based jump detection using IMUs achieved high sensitivity. Across studies, reliability indices were inconsistently reported, measurement error (SEM/MDC) was sparse, and MCID was not reported. The COSMIN appraisal ranged from very good/adequate to inadequate, driven primarily by small sample sizes, non-gold-standard comparators, and incomplete psychometric reporting. Full article

(1) Background: Gait analysis technologies have advanced; however, traditional systems like optical motion capture are lab-bound and costly, limiting rehabilitation monitoring. This cross-sectional study evaluates self-powered triboelectric nanogenerator (TENG) insoles combined with IMU sensors to assess gait asymmetry, plantar pressure signatures, age effects and injury history in rehabilitation patients, aiming to enable portable, battery-free phenotyping. (2) Methods: Fifty-three patients (22 females, 31 males; age, 29 ± 26 years) from Astana clinics with trauma histories (e.g., spine, ankle, fractures) and 10 healthy references underwent a 2 min walk test (2MWT). TENG insoles captured plantar loading; ankle/knee IMUs measured spatiotemporal parameters (cadence, asymmetry). The data were normalized; the analyses used an ANOVA and correlations (Python 3.14.3). (3) Results: The TENG sensors showed force/frequency linearity (up to 10 V at 20 N). The cadence averaged 101 ± 10 steps/min, declining with age (r = −0.31, p = 0.03) and fractures (r = −0.23, p = 0.04). The asymmetry varied (−54% to +31%) without category differences. Flatfoot (55%) was linked to lateral loading shifts; condition-specific waveform signatures emerged (e.g., lateral heel in ankle issues). (4) TENG-IMU systems feasibly capture gait phenotypes in heterogeneous cohorts, supporting out-of-lab monitoring for personalized rehabilitation without batteries. Prospective validation is required for further practical implications. Full article

The foot arch functions as a dynamic biomechanical system, maintained by the integrated actions of bones, ligaments, and muscles. A large body of clinical evidence indicates that, in addition to congenital foot deformities, acquired variations in the foot arch caused by factors such as poor gait, aging, weight, or injury can significantly affect quality of life. Early intervention upon detection of foot arch changes can help mitigate progression and prevent further deterioration. Despite the availability of multimodal sensor-integrated running platforms for gait analysis, such systems are inherently bulky and not conducive to routine walking measurement. To overcome the above limitations, this study employed a flexible plantar pressure insole with an integrated accelerometer and a dedicated acquisition circuit to capture plantar pressure and acceleration data. This smart insole system acquires plantar data, performs feature extraction via time–domain and wavelet analysis, and then employs machine learning to classify the foot arch type as a normal foot, flatfoot, or high-arched. A Random Forest classifier was then established to categorize foot arch types based on the collected data, which integrates numerous decision trees through bootstrap aggregation and random feature selection, with final classification determined by majority voting. A total of 30 volunteers participated, including 11 with normal arches, 11 with flat feet, and 8 with high arches. Compared with support vector machine, K nearest neighbors, and decision tree, the Random Forest achieved the highest recognition accuracy of 92%. This system reveals the patterns of plantar pressure distribution and acceleration fluctuations during walking across three foot arches and demonstrates that wavelet entropy can effectively quantify the changes in signal complexity included in foot arch differences. Compared with laboratory force plates, this system features lower cost and a smaller form factor, making it suitable for real-time monitoring. This system can lay the technical foundation for personalized foot orthopedics and health monitoring. Full article

Smart insoles equipped with pressure sensor matrices are increasingly used for gait analysis, yet high-density arrays compromise battery life and data throughput. This study aims to identify the optimal sparse sensor layout required to accurately estimate the Center of Pressure (CoP) by analyzing the trade-off between sensor number, spatial placement, and reconstruction error. Plantar pressure data were collected from twelve healthy participants walking at a self-selected speed using 16-sensor connected insoles. A combinatorial algorithm evaluated all $2^{16}-1$ possible sensor combinations to minimize the Root Mean Square Error (RMSE) in the antero-posterior, medio-lateral, and global Euclidean directions. Results reveal a non-linear convergence of accuracy that depends on the spatial axis. For longitudinal and global progression, a clear inflection point achieving sub-centimetric accuracy (RMSE < 5 mm) is reached at seven sensors. In contrast, medio-lateral tracking shows its largest discrete error reduction at five sensors, followed by gradual improvements at higher densities. Anatomical frequency analysis highlights distinct spatial requirements: the posterior heel is consistently selected for medio-lateral accuracy, while the lateral arch and metatarsal regions are critical for longitudinal progression. These findings suggest that while a minimum of seven strategically placed sensors enables robust CoP tracking across all spatial axes, optimal hardware design should remain task-specific. This work provides a data-driven framework for the development of energy-efficient wearable gait monitoring systems. Full article

Punch velocity is a key performance indicator in boxing and reflects effective coordination along the kinetic chain. This study aimed to investigate the relationship between punch acceleration and plantar pressure distribution using wearable sensing technologies. Twenty-four collegiate boxers (12 professional-level and 12 amateur-level athletes) performed jab and cross punches under controlled conditions. Punch acceleration was measured using a glove-mounted inertial measurement unit (IMU), while plantar pressure distribution was recorded using pressure-sensing insoles. Professional boxers demonstrated significantly higher punch acceleration (22–31%, p < 0.05) and greater forefoot plantar pressure (18–27%, p < 0.05) compared to amateur athletes. Correlation analysis revealed significant positive associations between forefoot pressure and punch acceleration (r = 0.62–0.71, p < 0.01), indicating that increased lower-limb force contributes to higher upper-limb striking performance. These findings demonstrate that combined wearable sensing provides a practical approach for quantifying punching biomechanics and identifying level-dependent kinetic-chain characteristics in boxing. Full article

Postural control is essential for daily function, and while stochastic resonance (SR) enhances balance in clinical populations, its efficacy in healthy young people remains underexplored. This study investigated (1) biomechanical effects of multisite plantar vibration on postural stability using center-of-pressure (CoP) parameters, and (2) short-term and sustained effects on balance performances. Phase 1 enrolled six participants to identify the optimal plantar stimulation configuration and to evaluate acute electromyographic responses under threshold-level vibration. Phase 2 evaluated long-term efficacy through an eight-week sham-controlled parallel-group randomized controlled trial. In this trial, eight participants received vibration combined with balance training, and another eight participants completed the same training protocol using sham insoles without vibration, analyzing CoP parameters (95% ellipse area, path length) and muscle activation (tibialis anterior, medial gastrocnemius, peroneus longus, extensor digitorum longus). Results showed full-site vibration reduced CoP area versus control (265.66 ± 188.6 mm² vs. 437.84 ± 190.95 mm², p < 0.05) without altering ankle muscle activation (all p > 0.05). Longitudinal analysis revealed CoP area reduction (−4.88 ± 10.42%) in the intervention group versus sham (p < 0.001), with maximum anterior displacement increasing by 25.03% during vibration (p < 0.05). Plantar white-noise vibration modulates CoP oscillations without neuromuscular activation changes, demonstrating that full-site stimulation acutely enhances postural stability while sustained intervention improves dynamic balance control. Full article

Prolonged standing and repetitive lifting are routine occupational stressors that elevate plantar pressures across workers. In those with diabetes, these demands represent additional risk factors for diabetic foot pathology, highlighting the need for ergonomic interventions beyond standard safety footwear. This study evaluated the perceived ergonomic performance of the MoonWalking^® insole, a novel adaptive pneumatic system designed for real-time pressure stabilization and offloading when integrated into safety footwear. A comparative experimental protocol tested two conditions: safety footwear with the manufacturer’s original insole and the same footwear with the MoonWalking prototype. Twenty participants assessed perceived comfort using a VAS and binary ergonomic questionnaires. The results showed statistically significant improvements in perceived cushioning, foot fit, and overall comfort when using the MoonWalking insole. Participants consistently identified pressure-stabilizing and offloading functions across all plantar regions, indicating that adaptive pressure control was clearly perceptible. No pain or movement restrictions were reported. Although perceived fatigue did not reach statistical significance, a decreasing trend was observed. A slight reduction in intention to reuse the footwear occurred with the prototype, possibly due to its increased weight. These findings provide evidence that integrating an adaptive pneumatic insole into safety footwear may improve plantar pressure redistribution and user comfort. Full article