Search Results (224)

In the study of biomechanical models, balance represents a complex problem due to the issue of indeterminate forces while standing. In order to solve this problem, it is essential to measure the ground reaction forces (GRFs) applied to each foot independently. The present work proposes a methodology for determining the independent GRF applied to each foot while standing when only one forceplate is available. For this purpose, an analytical method is proposed to determine the distribution of vertical GRFs and the position of the independent center of pressure (CoP) in each foot. Concurrently, several neural network (NN) models are trained to improve the results obtained. This hypothesis is experimentally validated by a self-developed device that allows one to simultaneously obtain the vertical GRF and CoP location of each foot at the same time that the GRF and the global CoP location are obtained from a single forceplate. The results obtained achieve a CoP position error of less than 8% and a vertical force error of 2%. The analytical hypothesis is demonstrated to offer a satisfactory level of precision, while the NN is shown to result in considerable improvement in some cases. Full article

Background: This study was performed to validate the addition of capacitive-based pressure sensors to an existing smart sock developed by the research team. This study focused on evaluating the accuracy of soft robotic sensor (SRS) pressure data and its relationship with laboratory-grade Kistler force plates in collecting ground force reaction data. Methods: Nineteen participants performed walking trials while wearing the smart sock with and without shoes. Data was collected simultaneously with the sock and the force plates for each gait phase including foot-flat, heel-off, and midstance. The correlation between the smart sock and force plates was analyzed using Pearson’s correlation coefficient and R-squared values. Results: Overall, the strength of the relationship between the smart sock’s SRS data and the vertical ground reaction force (GRF) data from the force plates showed a strong correlation, with a Pearson’s correlation coefficient of 0.85 ± 0.1; 86% of the trials had a value higher than 0.75. The linear regression models also showed a strong correlation, with an R-squared value of 0.88 ± 0.12, which improved to 0.90 ± 0.07 when including a stretch-SRS for measuring ankle flexion. Conclusions: With these strong correlation results, there is potential for capacitive pressure sensors to be integrated into the proposed device and utilized in telehealth and sports performance applications. Full article

Background: Residual deficits after early treatment of developmental dysplasia of the hip (DDH) using osteotomy often led to asymmetrical gait deviations with increased repetitive rates of ground reaction force (GRF) in both hips, resulting in a higher risk of early osteoarthritis. This study investigated lower limb inter-joint coordination and swing foot control during level walking in adolescents with early-treated unilateral DDH. Methods: Eleven female adolescents treated early for DDH using Pemberton osteotomy were compared with 11 age-matched healthy controls. The joint angles and angular velocities of the hip, knee, and ankle were measured, and the corresponding phase angles and continuous relative phase (CRP) for hip–knee and knee–ankle coordination were obtained. The variability of inter-joint coordination was quantified using the deviation phase values obtained as the time-averaged standard deviations of the CRP curves over multiple trials. Results: The DDH group exhibited a flexed posture with increased variability in knee–ankle coordination of the affected limb throughout the gait cycle compared to the control group. In contrast, the unaffected limb compensated for the kinematic alterations of the affected limb with reduced peak angular velocities but increased knee–ankle CRP over double-limb support and trajectory variability over the swing phase. Conclusions: The identified changes in inter-joint coordination in adolescents with early treated DDH provide a plausible explanation for the previously reported increased GRF loading rates in the unaffected limb, a risk factor of premature OA. Full article

Conventional ground reaction force (GRF) and load rate (LR) analyses may overlook temporal and waveform characteristics that reflect injury status and acuity. This study used an alternative GRF processing methodology to characterize GRF waveforms among runners with symptomatic medial tibial stress fractures (MTSS) and those recovering from tibial stress fractures (TSF; both unilateral [UL] and bilateral [BL]). This cross-sectional analysis of runners (n = 66) included four groups: symptomatic MTSS, recovering from UL or BL TSF, or uninjured case-matched controls. Participants ran at self-selected speed on an instrumented treadmill. Kinematics were collected with a 3D optical motion analysis system. Double-Gaussian models described the biphasic loading pattern of running gait (initial impact, active phases). Gaussian parameters described relative differences in the GRF waveform by injury condition. LR was calculated using the central difference numerical derivative of the raw normalized net force data. During the impact phase (0–20% of stance), controls and BL TSF produced higher GRF amplitudes than UL TSF and MTSS (p < 0.05). BL TSF and controls had greater maximal positive LR and minimum LR than UL TSF and MTSS. Peak medial GRF was 18–43% higher in the BL TSF group than in MTSS and UL TSF (p < 0.05). Correlations existed between tibial pain severity and early stance net GRF (r = 0.512; p = 0.016) and between pain severity and the duration since diagnosis for LR values during the impact phase (r values = 0.389–0.522; all p < 0.05). Collectively, these data suggest that this waveform modeling approach can differentiate injury status and pain acuity in runners. Early stance GRF and LR may offer novel insight into the management of running-related injuries. Full article

Vertical ground reaction force (vGRF) plays an important role in the study of running-related injuries (RRIs). This study explores the synchronization method between inertial measurement unit (IMU) and vGRF data of running and develops ANN models to accurately predict vGRF. Fifteen runners participated in this study. Acceleration data and vGRF values of eight rearfoot strikers and seven forefoot strikers running at 12, 14, and 16 km/h were collected by a single IMU and an instrumented treadmill. The sliding time window synchronization (STWS) algorithm was developed to sync IMU data with vGRF data. The wavelet neural network model (WNN) and feed-forward neural network model (FFNN) were adapted to predict vGRF using three-axis or sagittal-axis acceleration data in the stance phase, respectively. One rearfoot striker and one forefoot striker were randomly selected as a test set, while the other participants formed training sets. After synchronization, mean absolute errors for stride time of the IMU and vGRF data were less than 11.2 ms. The coefficient of multiple correlations for vGRF measured curves and predicted curves was more than 0.97. The normalized root mean square errors (NRMSEs) between two curves were 4.6~9.2%, and R² was 0.93~0.99. For peak vGRF, the NRMSEs were 1.6~8.2%, except for rearfoot strike runners at 16 km/h using the FFNN model (10.7% and 11.1%). The Bland–Altman plots indicate that the errors for both the WNN and FFNN models are within acceptable limits. The STWS algorithm can effectively achieve the data synchronization between the IMU and the force plate during running. Both WNN and FFNN models demonstrated good accuracy and agreement in predicting vGRF. Using sagittal-axis acceleration data may be an ideal model with good prediction accuracy and less input data. This work provides direction for developing ANN models of personalized monitoring of lower limb load. Full article

The foot deformation approach (FDA) estimates the ground reaction force (GRF) and moment (GRM) from kinematic data with practical accuracy, low computational cost, and no requirement for training data. Our previous study demonstrated practical estimation accuracy of the FDA under barefoot conditions. However, since the FDA estimates GRFs and GRMs based on foot deformation under body weight, there are concerns about its applicability to footwear conditions, where the foot deformation characteristics differ from those of bare feet. Following the issue, this study conducted a walking experiment at three different speeds with running shoes and sneakers to investigate the impact of footwear on GRF prediction using the FDA. The results showed that the FDA successfully provided practical accuracy when shoes were worn, comparable to that for a barefoot participant. The FDA offers advantages for estimating GRFs and GRMs for the footwear condition, while eliminating the need for collecting training data and enabling rapid analysis and feedback in clinical settings. Although the FDA cannot fully eliminate the effects of footwear and movement speed on prediction accuracy, it has the potential to serve as a convenient biomechanical-based method for estimating GRFs and GRMs during sports and daily activities with footwear. Full article

Objective: We developed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) based on marker trajectories obtained from a markerless motion capture system. Methods: Gait data were collected using two cameras and three force plates. Each gait dataset contained kinematic data and kinetic data from the stance phase. A multi-layer perceptron (MLP) and convolutional neural network (CNN) were constructed to estimate each component of GRF and COP based on the three-dimensional trajectories of the markers. A total of 100 samples were randomly selected as the test set, and the estimation performance was evaluated using the correlation coefficient (r) and relative root mean square error (rRMSE). Results: The r-values for MLP in each GRF component ranged from 0.918 to 0.989, with rRMSEs between 5.06% and 12.08%. The r-values for CNN in each GRF component ranged from 0.956 to 0.988, with rRMSEs between 6.03–9.44%. For the COP estimation, the r-values for MLP ranged from 0.727 to 0.982, with rRMSEs between 6.43% and 27.64%, while the r-values for CNN ranged from 0.896 to 0.977, with rRMSEs between 6.41% and 7.90%. Conclusions: It is possible to estimate GRF and COP from markerless motion capture data. This approach provides an alternative method for measuring kinetic parameters without force plates during gait analysis. Full article

Traditional force plate-based systems offer high measurement precision but are limited to laboratory settings, restricting their use in real-world environments. To address this, we propose a method for estimating a three-axis ground reaction force (GRF) and two-axis center of pressure (CoP) using a shoe embedded with three uniaxial load cells. The estimation was conducted under five gait conditions: straight walking, turning, uphill, downhill, and running. Data were collected from 40 healthy young adults. Four deep-learning models—Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Sequence-to-Sequence Long Short-Term Memory (Seq2Seq-LSTM), and Transformer—were evaluated. Among them, Seq2Seq-LSTM and CNN achieved the highest performance in predicting both GRF and CoP. However, the medio-lateral (ML) components showed lower accuracy than the vertical and anterior–posterior directions. In slope conditions, particularly for vertical GRF, relatively higher root mean-square error (RMSE) values were observed. Despite some variation across gait types, predicted values showed high agreement with measurements. Compared with previous studies, the proposed method achieved comparable or better performance with a minimal sensor setup. These findings highlight the feasibility of accurate GRF and CoP estimation in diverse gait scenarios and support the potential for real-world applications. Future work will focus on sensor optimization and broader population validation. Full article

Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to modeling errors. In contrast, direct sensing of contact forces at the feet provides more accurate and immediate feedback. Beyond force magnitude, tactile sensing also enables richer contact interpretation, such as detecting force direction and surface properties. In this work, we show how tactile sensor information can be used inside the feedback of the control loop to achieve compliance of legged robots during ground contact. The three main contributions are (i) a fast and computationally efficient 3D force reconstruction method tailored for spherical tactile sensors, (ii) a tactile admittance controller that adjusts leg motions to achieve the desired GRFs and compliance, and (iii) experimental validation on a quadruped robot, demonstrating enhanced load distribution and balance during external perturbations and locomotion. The results show that the peak ground reaction forces were reduced by 55% while balancing on a beam. During a locomotion scenario involving sudden touchdown after a fall, the tactile admittance controller reduced oscillations and regained stability compared to proportional–derivative (PD) control. Full article

The evolution of motion capture technology from marker-based to marker-less systems is a promising field, emphasizing the critical role of combining electromyography (EMG), joint moments, and ground reaction forces (GRF) in advancing biomechanical analysis. This review examines the integration of EMG, joint moments, and GRF in marker-less video-based motion capture systems, focusing on current approaches, challenges, and future research directions. This paper recognizes the significant challenges of integrating the aforementioned modalities, which include problems of acquiring and synchronizing data and the issue of validating results. Particular challenges in accuracy, reliability, calibration, and environmental influences are also pointed out, together with the issue of the standard protocols of multimodal data fusion. Using a comparative analysis of significant case studies, the review examines existing methodologies’ successes and weaknesses and established best practices. New emerging themes of machine learning techniques, real-time analysis, and advancements in sensing technologies are also addressed to improve data fusion. By highlighting both the limitations and potential advancements, this review provides essential insights and recommendations for future research to optimize marker-less motion capture systems for comprehensive biomechanical assessments. Full article

Background and Objectives: The increased prevalence of knee osteoarthritis (OA) and need for total knee arthroplasty (TKA) indicate a growing need for effective prehabilitation. The effect of preoperative home exercise programs (HEPs) on gait in patients with severe knee OA is under-investigated. This study aimed to evaluate the influence of an 8-week preoperative HEP on gait characteristics, leg extensor muscle strength, knee function, and health status in women with severe knee OA scheduled for TKA and to compare them with healthy control data. Material and Methods: Eighteen women with severe knee OA (KOA, aged 61.8 ± 1.6 years) and ten age-matched healthy women (CON) participated in this study. The KOA group performed an HEP with 15 exercises aimed at improving lower limb muscle strength, motion, balance, and coordination. Gait spatiotemporal and kinetic characteristics during the loading response, isometric leg extensor strength, knee active range of motion (AROM), and The Western Ontario and McMaster Universities Arthritis Index (WOMAC) were investigated. Associations between characteristics were analyzed. Results: Improvements in ground reaction force (GRF) during the loading response of gait, leg extensor muscle strength, the knee AROM, and the WOMAC index were found post-HEP. The KOA group demonstrated lower (p < 0.05) spatiotemporal and GRF characteristics than the CON group. Knee extension moment (KEM) was lower pre-HEP (p < 0.05) but did not differ significantly from the CON group post-HEP. Gait characteristics and WOMAC were associated with leg extensor muscle strength and knee AROM and pain in the KOA group. Conclusions: An eight-week preoperative HEP improved GRF and KEM during the loading response of gait, muscle strength, knee function, and self-reported knee OA-related health status in women with severe knee OA. Preoperative HEP before TKA, focusing on leg extensor muscle strength, range of motion, and pain relief, is an effective alternative to supervised exercise therapy in women with severe knee OA. Full article

Ground reaction force (GRF) and center of pressure (COP) during walking are two important measures that could be used in a range of applications, from the control of devices such as exoskeletons to clinical assessments. Recording these measures requires fixed laboratory equipment such as force plates or expensive portable insoles. We present an alternative approach by developing a 3D-printed insole that uses pneumatic chambers and pressure sensors to estimate the net GRF and the anterior–posterior COP position. The intentionally simple design, using just two pneumatic chambers, can be fabricated using standard 3D printing technology and readily available soft materials. We used experimentally recorded data from a motion capture system along with parameter identification techniques to characterize and validate the insole while walking at different speeds. Our results showed that the insole was capable of withstanding repeated loading during walking—up to 1.2 times the body weight—and possessed a bandwidth high enough to capture gait dynamics. The identified models could estimate the GRF and the anterior–posterior COP position with less than 9% error. These results compare favourably with those of commercially available instrumented insoles and can be obtained at a fraction of their cost. This low-cost yet effective solution could assist in applications where it is important to record gait outside of laboratory conditions, but the cost of commercial solutions is prohibitive. Full article

Instrumented insoles have created opportunities for patient monitoring via long-term recordings of ground reaction forces (GRFs). As the GRF curve is altered in patients after lower-extremity fracture, parameters defined on established curve landmarks often cannot be used to monitor the early rehabilitation process. We aimed to screen several new GRF curve-based parameters for suitability and hypothesized an interrelation with days after surgery. In an observational longitudinal study, data were collected from 13 patients with tibial fractures during straight walking at hospital visits using instrumented insoles. Parametrized curves were fitted and regression analyses conducted to determine the best fit, reflected in the highest R²-value and lowest fitting error. A Wald Test with t-distribution was employed for statistical analysis. Strides were classified as regular or non-regular, and changes in this proportion were analyzed. Among the 12 parameters analyzed, those with the highest R²-values were the mean force between inflection points (R² = 0.715, p < 0.001, t₄₂ = 9.89), the absolute time between inflection points (R² = 0.707, p < 0.001, t₄₂ = 9.83), and the highest overall force (R² = 0.722, p < 0.001, t₄₂ = 10.05). There was a significant increase in regular strides on both injured (R² = 0.427, p < 0.001, t₄₂ = 5.83) and healthy (R² = 0.506, p < 0.001, t₄₂ = 6.89) sides. The proposed parameters and assessment of the regular stride ratio enable new options for analyses and monitoring during rehabilitation after tibial shaft fractures. They are robust to pathologic GRF curves, can be determined independently from spatiotemporal coherence, and thus might provide advantages over established methods. Full article

Objectives: This study aimed to investigate the effects of combined supplementation with Rhodiola rosea (RHO) and caffeine (CAF) on the explosive power and sustained output capacity of lead and rear straight punches in both untrained and trained volunteers, with a focus on potential synergistic effects. Methods: randomized, double-blind, placebo-controlled design was employed, enrolling 96 participants (48 untrained, 48 trained). Participants were stratified and randomly assigned to the control (CTR), CAF, RHO, or CAF+RHO group. All subjects completed an 8-week standardized boxing training program (twice per week). Punch performance was assessed using professional boxing equipment and a biomechanical testing system, evaluating lead and rear straight punches, ground reaction force (GRF), and a 30 s continuous punching test. Results: the CAF+RHO  group showed significant improvements in both untrained and trained volunteers. Com-pared to the RHO group, this group demonstrated higher lead punch velocity, shorter bi-lateral peak force time during rear punches, and more punches in the 30 s test (p < 0.05). Compared to the CAF group, the CAF+RHO group exhibited greater rear punch force, higher bilateral peak force during lead punches, increased forefoot peak force in rear punches, and improved 30 s power output (p < 0.05). The CAF+RHO group also outperformed the CTR group across all parameters (p < 0.05). Conclusions: Combined supple mentation with CAF and RHO significantly enhances both explosive power and sustained output in boxing performance. This may result from improved energy metabolism efficiency and neuromuscular coordination, providing a promising nutritional strategy for high-intensity intermittent exercise. Full article

Background/Objectives: Currently, there is no strong evidence to support interventions for medial tibial stress syndrome (MTSS), a common running injury associated with tibial loading. Vertical ground reaction force (vGRF) and axial tibial acceleration (TA) are the most common methods of estimating tibial loads, yet clinical recommendations for technique modification to reduce these metrics are not well documented. This study investigated whether changes to speed, cadence, stride length, and foot-strike pattern influence vGRF and TA. Additionally, machine-learning models were evaluated for their ability to estimate vGRF metrics. Methods: Sixteen runners completed seven 1 min trials consisting of preferred technique, ±10% speed, ±10% cadence, forefoot, and rearfoot strike. Results: A 10% speed reduction decreased peak tibial acceleration (PTA), vertical average loading rate (VALR), vertical instantaneous loading rate (VILR), and vertical impulse by 13%, 10.9%, 9.3%, and 3.2%, respectively. A 10% cadence increase significantly reduced PTA (11.5%), VALR (15.6%), VILR (13.5%), and impulse (3.5%). Forefoot striking produced significantly lower PTA (26.6%), VALR (68.3%), and VILR (68.9%). Habitual forefoot strikers had lower VALR (58.1%) and VILR (47.6%) compared to rearfoot strikers. Machine-learning models predicted all four vGRF metrics with mean average errors of 9.5%, 10%, 10.9%, and 3.4%, respectively. Conclusions: This study demonstrates that small-scale modifications to running technique effectively reduce tibial load estimates. Machine-learning models offer an accessible, affordable tool for gait retraining by predicting vGRF metrics without reliance on IMU data. The findings support practical strategies for reducing MTSS risk. Full article