Search Results (163)

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

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

Background: Diabetic foot ulcers (DFUs) are considered a prevalent complication of diabetes mellitus, frequently accompanied with compromised peripheral circulation, slower healing, as well as high risk of infection in addition to risk of amputation. Additional treatments that enhance microvascular perfusion and lessen plantar pressure may accelerate the healing process. This study was carried out to examine the impact of pulsed electromagnetic field (EMF) therapy as well as customized silicone gel insoles in terms of peripheral circulation in addition to vascular indices in patients with DFUs. Methods: A randomized, controlled clinical trial, including sixty-six adults diagnosed with type II diabetes as well as plantar DFUs (Wagner grade I–II) were divided into three groups (n = 22 each): Group A was given low-frequency electromagnetic field therapy (15–50 Hz, 2–5 mT, 30 min, three times per week for 8 weeks), Group B was given a customized silicone gel insoles produced for ulcer offloading, and Group C (control) was given conventional physiotherapy along with wound care. Peripheral microcirculation as well as tissue perfusion were the primary outcomes, and they were measured using Laser Doppler Flowmetry (LDF), Photoplethysmography (PPG), in addition to the Toe–Brachial Index (TBI). The secondary outcome included the Ankle–Brachial Pressure Index (ABPI). A blinded assessor measured the outcomes at the beginning of the study, after the intervention (week 8), and again after the follow-up (week 16). Results: EMF therapy significantly improved LDF (baseline: 45.2 ± 6.5 PU; week 8: 62.5 ± 7.2 PU), PPG (0.42 ± 0.08 mV to 0.68 ± 0.10 mV), TBI (0.64 ± 0.07 to 0.82 ± 0.08), and ABPI (0.88 ± 0.06 to 0.97 ± 0.05) compared with insoles and controls (p < 0.001, partial η² 0.25–0.37). The insole group exhibited moderate enhancements, whereas the control group demonstrated minor changes. Between-group analyses showed substantial differences in favor of EMF therapy across all measured variables (F = 13.5–19.9, p < 0.001). Improvements continued at the 8-week follow-up. Conclusions: Patients with DFUs who receive EMF therapy experience a significant improvement in their peripheral microcirculation, tissue perfusion, as well as vascular indices. This is more effective than just mechanical offloading, and custom insoles offer extra benefits by redistributing pressure. Combining EMF therapy with regular DFU care may speed up healing and lower the risk of problems. Additional research should investigate the efficacy of combined EMF as well as off-loading interventions and their long-term outcomes. Full article

Introduction: Anterior cruciate ligament (ACL) injuries are among the most severe and frequent injuries in alpine skiing, often occurring in non-contact situations during high-demand turns. Various instrumental techniques were used to assess susceptibility to anterior cruciate ligament (ACL) injuries in alpine ski racers and recreational skiers. This cross-sectional exploratory study aimed to identify key factors contributing to ACL injury susceptibility, comparing lab-based and on-snow tests. Materials and Methods: We examined nine elite ski racers and nine recreational skiers with strong athletic backgrounds. Skiing technique was analyzed using an instrumented insole system (CARV) to measure body position, pressure symmetry, and edge angle. Dynamic Q-angle symmetry during single-leg squats were assessed with an optical system (DynaKnee), while balance, strength, and agility were evaluated through ACL-specific lab tests (CoRehab). Group comparisons were performed using the nonparametric Mann–Whitney U test. Results: No significant differences were found between groups in ACL-specific lab tests, including balance, agility, and jump performance. However, ski racers exhibited 34.9% higher asymmetry in the Q-angle symmetry index during the one-leg squat. In contrast, ski technique differences were significant: ski racers achieved 16.3% higher Edge Similarity, 48% better Pressure Symmetry, and 5.8% better Fore-Aft Balance compared to recreational skiers. Conclusions: Despite similar general athletic abilities, elite skiers showed higher Q-angle asymmetry, which has been previously associated with ACL injury risk. However, their advanced skiing technique may partially mitigate the functional consequences of this asymmetry during on-snow tests. This suggests that refined skiing skills may influence functional performance in racing conditions, while pronounced one-sided dominance could indicate potential injury risk. Full article

Background: Walking cadence is commonly adjusted in sport and rehabilitation, yet its effects on spatiotemporal gait parameters and regional plantar pressure distribution under controlled speed conditions remain incompletely characterized. Therefore, this study aimed to determine whether imposed cadence increases at a constant walking speed would (i) systematically reduce temporal gait parameters while preserving inter-limb symmetry and (ii) be associated with region-specific increases in forefoot plantar loading, representing the primary novel contribution of this work. Methods: Fifty-two adults walked at three imposed cadences (110, 120, 130 steps·min⁻¹) while maintaining a fixed treadmill speed of 1.39 m·s⁻¹ via auditory biofeedback. Spatiotemporal parameters were recorded with an OptoGait system, and plantar pressure distribution was measured using in-shoe pressure insoles. Normally distributed variables were analyzed using repeated-measures ANOVA, whereas plantar pressure metrics were assessed using the Friedman test, followed by Wilcoxon signed-rank post-hoc comparisons with false discovery rate (FDR) correction. Associations between temporal parameters and plantar loading metrics (peak pressure, pressure–time integral) were examined using Spearman’s rank correlation with FDR correction (α = 0.05). Results: Increasing cadence produced progressive reductions in gait cycle duration (~8–10%), contact time (~7–8%), and step time (all p < 0.01), while inter-limb symmetry indices remained below 2% across conditions. Peak plantar pressure increased significantly in several forefoot regions with increasing cadence (all p_FDR < 0.05), whereas changes in the first ray were less consistent across conditions. Regional forefoot pressure–time integral also increased modestly with higher cadence (p_FDR < 0.01). Spearman’s correlations revealed moderate negative associations between temporal gait parameters and global plantar loading metrics (ρ = −0.38 to −0.46, all p_FDR < 0.05). Conclusions: At a constant walking speed, increasing cadence systematically shortens temporal gait components and is associated with small but consistent region-specific increases in forefoot plantar loading. These findings highlight cadence as a key temporal constraint shaping plantar loading patterns during steady-state walking and support the existence of concurrent temporal–mechanical adaptations. Full article

Efficient monitoring of lower-limb coordination is important for understanding movement characteristics during off-ice speed-skating training. This study aimed to develop an analytical framework to characterize the kinematic–kinetic coupling of the lower limbs during slideboard skating tasks using wearable sensors. Eight national-level junior speed skaters performed standardized simulated skating movements on a slideboard while wearing sixteen six-axis inertial measurement units (IMUs) and Pedar-X in-shoe plantar-pressure insoles. Joint-angle trajectories and plantar-pressure signals were temporally synchronized and preprocessed using a Kalman-based multimodal state-estimation approach. Third-order polynomial regression models were applied to examine the nonlinear relationships between hip–knee joint angles and plantar loading across four distinct movement phases. The results demonstrated consistent coupling patterns between angular displacement and peak plantar pressure across phases (R² = 0.72–0.84, p < 0.01), indicating coordinated behavior between joint kinematics and plantar kinetics during simulated skating movements. These findings demonstrate the feasibility of a Kalman-based joint analysis framework for fine-grained assessment of lower-limb coordination in slideboard speed-skating training and provide a methodological basis for future investigations using wearable sensor systems. Full article

(1) Background: Navigating surfaces during walking can alter gait patterns. This study aims to develop tools for automatic walking condition classification using inertial measurement unit (IMU) and foot pressure sensors. We compared sensor modalities (IMUs on lower-limbs, IMUs on feet, IMUs on the pelvis, pressure insoles, and IMUs on the feet or pelvis combined with pressure insoles) and evaluated whether gait cycle segmentation improves performance compared to a sliding window. (2) Methods: Twenty participants performed flat, stairs up, stairs down, slope up, and slope down walking trials while fitted with IMUs and pressure insoles. Machine learning (ML; Extreme Gradient Boosting) and deep learning (DL; Convolutional Neural Network + Long Short-Term Memory) models were trained to classify these conditions. (3) Results: Overall, a DL model using lower-limb IMUs processed with gait segmentation performed the best ($F1=0.89$). Models trained with IMUs outperformed those trained on pressure insoles ($p<0.01$). Combining sensor modalities and gait segmentation improved performance for ML models ($p<0.01$). The best minimal model was a DL model trained on IMU pelvis + pressure insole data using sliding window segmentation ($F1=0.83$). (4) Conclusions: IMUs provide the most discriminative features for automatic walking condition classification. Combining sensor modalities may be helpful for some model architectures. DL models perform well without gait segmentation, making them independent of gait event identification algorithms. Full article

Exercise fatigue is a critical factor that compromises athletic performance, increases the risk of musculoskeletal injury, and threatens safety in military and occupational settings. Reliable monitoring of fatigue is therefore essential for optimizing training, preventing injury, and safeguarding long-term health. Biomechanical indicators, including joint kinematics, ground reaction forces, and electromyographic signals, provide valuable insight into the biomechanical manifestations of fatigue. Although traditional laboratory-based methods are accurate, they are costly, cumbersome, and unsuitable for continuous field monitoring. Recent advances in wearable technologies, particularly inertial measurement units (IMUs), insole pressure sensors (IPSs), and surface electromyography (sEMG), enable continuous, noninvasive, and real-time assessment of biomechanical changes during exercise fatigue. This review synthesizes current progress in IMU-, IPS-, and sEMG-based wearable systems for biomechanical exercise fatigue monitoring, highlighting their principles, strengths, and challenges. Full article

Center of pressure is a valuable biomechanical variable, predicting joint loading contributions during movement and giving insight into compensatory patterns. The purpose of this study was to assess the validity and reliability of force insoles in calculating vertical ground reaction force and center of pressure during return-to-sport jump testing. Ten healthy individuals performed double- and single-leg vertical and horizontal jumps on an instrumented treadmill while wearing instrumented force insoles. Vertical ground reaction force and anterior–posterior and medial–lateral center of pressure were collected at peak vertical ground reaction force from both devices. Repeat testing occurred 7 ± 5 days following the initial session. Force insoles were valid for measuring vertical ground reaction force (mean absolute error (MAE): 4.34 N/kg) and anterior–posterior center of pressure (MAE: 10% foot length) but were not valid for medial–lateral center of pressure (MAE: 50% foot width). During double-leg vertical, single-leg vertical, double-leg horizontal, and single-leg horizontal jumps, force insoles demonstrated good reliability for measurements of vertical ground reaction force (ICC: 0.89, 0.75, 0.89, and 0.91), anterior–posterior center of pressure (ICC: 0.88, 0.89, 0.94, and 0.97), and medial–lateral center of pressure (ICC: 0.72, 0.09, 0.82, and 0.73). Force insoles are a valid and reliable alternative to evaluating vertical ground reaction force and anterior–posterior center of pressure during return-to-sport jump testing. Full article

Objective: Plantar pressure abnormalities have a significant impact on mobility and quality of life. Real-time pressure monitoring is essential in clinical and rehabilitation settings for assessing patient progress and refining treatment protocols. Instrumental and particularly smart insoles offer a promising solution by collecting biomechanical data during daily activities. However, determining the optimal combination of sensor type, number, and placement remains a key challenge for ensuring accurate and reliable measurements. This study proposes a methodology for identifying the most appropriate sensor technology for wearable insoles, with a focus on data accuracy, system efficiency, and practical applicability. Additionally, it examines the correlation between sensor signals and material behavior during compression testing. Methods: Two insole prototypes underwent compression testing: one equipped with a Force Sensitive Resistor (FSR) sensor and one with a piezoelectric sensor, both positioned at the heel. Three trials per prototype assessed consistency and repeatability. Real-time data acquisition utilized a microcontroller system, and signals were processed using a sixth-order Butterworth low-pass filter with a 5 Hz cutoff frequency to reduce noise. Results: FSR sensors demonstrated stable static responses but saturated rapidly beyond 20 N, with performance degradation observed after repeated loading cycles. Piezoelectric sensors exhibited excellent dynamic sensitivity with sharp voltage peaks but proved unable to measure sustained static pressure. Conclusions: FSR sensors are well-suited for static postural assessment and continuous pressure monitoring, while piezoelectric sensors excel in dynamic gait analysis. This comparative framework establishes a foundation for developing future smart insole systems that deliver accurate, real-time rehabilitation monitoring. Full article

Introduction: Numerous overuse injuries affecting the lower limbs of elite athletes have been associated with biomechanical alterations in plantar loading of the foot. This study aimed to analyze the plantar pressure distribution in elite male soccer players and its relationship with various morphological and functional factors, including foot type, metatarsal and digital alignment, and on-field position. Material and Method: Dynamic foot pressure measurements were obtained from 21 soccer players who participated in the UEFA Champion League. The participants had an average age of 27 years, with an average height of 180.9 cm, weight of 76.9 kg, and BMI of 23.4. An insole system (BioFoot/IBV) with telemetry transmission was employed to record plantar loading patterns during normal gait and running. Results: During the support or contact phase, the central and medial metatarsal areas exhibited the highest peak pressure under both walking and running conditions. When walking, the right foot exerted 13–60% more pressure on the outer metatarsal and toe areas. The left foot experienced up to 13% more peak pressure in the middle metatarsal area. During running, the total pressure difference between the feet ranged from −8% to +19%. The right foot usually had more peak pressure on the heel and first toe. In players with valgus feet, the pressure in the central metatarsal area increased from 1086 kPa (walking) to 1490 kPa (running), representing a 37% increase. Conversely, in players with cavus-varus feet, the pressure in this central area increased from 877 kPa to 1804 kPa, a 105% increase. Conclusions: Foot morphology and playing position significantly influenced the plantar pressure patterns in elite soccer players. The central metatarsal region bears the highest load, particularly during running, with distinct variations across foot types and field positions. These findings highlight the need for individualized biomechanical assessments to prevent overuse injuries and optimize performance. Full article

Background/Objectives: Multiple sclerosis (MS) often leads to gait impairments, even in early stages, and can affect autonomy and quality of life. Traditional assessment methods, while widely used, have been criticized because they lack sensitivity to subtle gait changes. This scoping review aims to map the landscape of advanced gait analysis technologies—both wearable and non-wearable—and evaluate their application in detecting, characterizing, and monitoring possible gait dysfunction in individuals with MS. Methods: A systematic search was conducted across PubMed and Scopus databases for peer-reviewed studies published in the last decade. Inclusion criteria focused on original human research using technological tools for gait assessment in individuals with MS. Data from 113 eligible studies were extracted and categorized based on gait parameters, technologies used, study design, and clinical relevance. Results: Findings highlight a growing integration of advanced technologies such as inertial measurement units, 3D motion capture, pressure insoles, and smartphone-based tools. Studies primarily focused on spatiotemporal parameters, joint kinematics, gait variability, and coordination, with many reporting strong correlations to MS subtype, disability level, fatigue, fall risk, and cognitive load. Real-world and dual-task assessments emerged as key methodologies for detecting subtle motor and cognitive-motor impairments. Digital gait biomarkers, such as stride regularity, asymmetry, and dynamic stability demonstrated high potential for early detection and monitoring. Conclusions: Advanced gait analysis technologies can provide a multidimensional, sensitive, and ecologically valid approach to evaluating and detecting motor function in MS. Their clinical integration supports personalized rehabilitation, early diagnosis, and long-term disease monitoring. Future research should focus on standardizing metrics, validating digital biomarkers, and leveraging AI-driven analytics for real-time, patient-centered care. Full article

Classifying human locomotor activities from wearable sensor data is an important high-level component of control schemes for many wearable robotic exoskeletons. In this study, we evaluated three machine learning models for classifying activity type (walking, running, jumping), speed, and surface incline using input data from body-worn inertial measurement units (IMUs) and e-textile insole pressure sensors. The IMUs were positioned on segments of the lower limb and pelvis during lab-based data collection from 16 healthy participants (11 men, 5 women), who walked and ran on a treadmill at a range of preset speeds and inclines. Logistic Regression (LR), Random Forest (RF), and Light Gradient-Boosting Machine (LGBM) models were trained, tuned, and scored on a validation data set (n = 14), and then evaluated on a test set (n = 2). The LGBM model consistently outperformed the other two, predicting activity and speed well, but not incline. Further analysis showed that LGBM performed equally well with data from a limited number of IMUs, and that speed prediction was challenged by inclusion of abnormally fast walking and slow running trials. Gyroscope data was most important to model performance. Overall, LGBM models show promise for implementing locomotor activity prediction from lower-limb-mounted IMU data recorded at different anatomical locations. Full article