Search Results (77)

Background: The integration of markerless motion capture systems such as OpenCap with force platforms expands the possibilities of biomechanical analysis in low-cost environments; however, it requires robust temporal synchronization procedures in the absence of shared hardware triggers. Objective: To develop and validate an automatic synchronization algorithm based on heel kinematic events to align OpenCap data with force platform signals during lower-limb functional exercises. Methods: Thirty normal-weight adult women (18–45 years) were evaluated while performing between 11 and 14 functional tasks (60° and 90° squats, lunges, sliding variations, and step exercises), yielding 330 motion records. Kinematics were estimated using OpenCap (four iPhone 12 cameras at 60 Hz), and kinetics were recorded using BTS P6000 force platforms synchronized with an OptiTrack system (Gold Standard). The algorithm detected heel contact from the filtered vertical coordinate and aligned this event with the initial rise in vertical ground reaction force. Validation against the Gold Standard was performed in 20 squat repetitions (10 at 60° and 10 at 90°) using Pearson correlation, RMSE, and MAE of the time-normalized and amplitude-normalized (0–1) vertical ground reaction force (vGRF). Results: The algorithm successfully synchronized 92.5% of the 330 records; the remaining cases showed kinematic noise or additional steps that prevented robust event detection. During validation, correlations were r = 0.85 (60°) and r = 0.81 (90°), with Root Mean Square Error (RMSE) < 0.17 and Mean Absolute Error (MAE) < 0.14, values representing less than 0.1% of the peak force. Conclusions: The heel-contact-based algorithm allows accurate synchronization of OpenCap and force platform signals during lower-limb functional exercises, achieving performance comparable to hardware-synchronized systems. This approach facilitates the integration of markerless motion capture in clinical, sports, and occupational settings where advanced dynamic analysis is required with limited infrastructure. Full article

Gait events (instant of heel strikes and instant of toe-offs) are essential for extracting spatiotemporal parameters and segmenting biological signals (electromyography (EMG) and electroencephalography (EEG)) based on gait cycle. While force platforms and optical motion capture (OMC) are ideal for identifying GE, inertial measurement units (IMUs) are more applicable. This study compared the accuracy and variability from IMU- and OMC-based gait event detection methods compared with gold-standard ground reaction force (GRF) detection. Seventeen healthy adults (31 ± 8 years) walked along a 10 m walkway instrumented with force plates. Foot kinematics were recorded using two retro-reflective markers on each foot and an IMU on the sacrum. Gait events were identified using two OMC-based (OMC1, OMC2) and two IMU-based (IMU1, IMU2) algorithms. Accuracy was evaluated using root-mean-square error (RMSE) relative to GRF, and within-subject variability was assessed using coefficient of variation (CoV). The results from the instant of heel strikes, OMC1 yielded a lower RMSE (14 ms) than IMU1 (50 ms) and IMU2 (61 ms) (p < 0.001). For the instant of toe-offs, OMC1 demonstrated a lower RMSE (17 ms), differing from IMU1 (54 ms) and IMU2 (74 ms) (p < 0.001). IMU2 exhibited greatest variability (CoV = 24 ms) compared with OMC1 (7 ms) and IMU1 (9 ms) (p < 0.001). Our results highlight lower accuracy and higher variability in gait event detection using sacrum-mounted IMUs. Despite its convenience, researchers should consider the limitations of using IMUs for EMG/EEG data segmentation. Future studies validating gait event detection methods should report some type of variability metric. Full article

Background: We aimed to analyze various gait parameters before and after THA for patients with a highly dislocated hip to examine gait recovery and whether it is continued. Methods: This was a retrospective, single-center study. We enrolled 10 patients with a highly dislocated hip (10 hips) due to developmental dysplasia of the hip (DDH) or sequelae of septic arthritis of the hip (SSH). A spatio-temporal gait analysis was performed before THA with subtrochanteric osteotomy and one year after surgery for all patients, and 5 of them had a complete follow-up gait analysis at five years postoperatively. Demographics, clinical outcome, and radiological data were collected. Results: At one year postoperatively, the terminal double support (TDS) increased from 8.6% (4.3–12.6) to 11.3% (5.8–14.0) of the gait cycle (p = 0.02). The vertical ground reaction force (vGRF) increased from 0.96 N/BW (0.69–1.30) to 1.11 N/BW (0.95–1.31) for the first peak (p = 0.045) and from 0.87 N/BW (0.59–1.12) to 1.10 N/BW (1.00–1.30) for the second peak (p = 0.001). However, there was no improvement in any gait parameters at five years postoperatively compared to one year postoperatively. The mean HHS was 57.2 (43–67) before surgery and 79.6 (61–88) at the last follow-up (p = 0.001). The preoperative leg length discrepancy (LLD), which was 43.6 mm (18.2–71.6), and improved to 9.8 mm (2.1–22.1) after surgery. Conclusions: Improvements in stance-phase stability (TDS) and vertical ground reaction forces (vGRF) enhanced gait after THA in patients with highly dislocated hips; however, these gains were only observed until 1 year postoperatively, with no further improvement thereafter. Notably, the magnitude of improvement in TDS and vGRF may exceed that typically reported after THA for primary osteoarthritis. Full article

During an unpredictable side-cut, this study examined how a sport-specific neuromuscular training program (NMTP) influenced electromyography responses in the lower limb posterior muscles, leg movement angles, maximum vertical ground reaction force (vGRF), and the rate of force development of vGRF. Thirty-eight adult female recreational hockey players were randomly allocated into an intervention group (INT) or a control group (CON). Before beginning training or matches, the INT carried out the NMTP three times per week for eight weeks, whereas the CON performed their routine warm-up. A 45° sidecut (dominant leg only) was performed at baseline and after eight-weeks and recorded with a motion capture system. The effect of group and time, and their interaction, was investigated using a mixed-design ANOVA. After landing, the participants in the INT had greater activation of their gastrocnemius lateralis, gastrocnemius medialis, and gluteus maximus muscles than those in the CON. INT participants showed significantly lower amounts of maximum knee abduction and knee excursion, while there was an increase in these variables for the CON. At week eight, the vGRF RFD decreased for the INT but increased for the CON. Although non-significant, the overall muscle activity showed an increasing trend for the INT when it came to supervised NMTP for eight weeks compared to the effect seen in the CON. This activity caused greater alterations in the motion and forces of the lower body for the INT than the CON. Full article

Background/Objectives: Understanding how muscle fatigue contributes to musculoskeletal injuries is critical in sports science. Although joint biomechanics during landing under fatigue has been studied before, limited research has focused on the layup phase under fatigue. This study examined the effects of fatigue on ankle, knee, and hip-joint biomechanics during layup and landing. We hypothesized that fatigue would increase peak vertical ground reaction force (GRF), peak knee extension angle, and peak joint moments. Methods: Fourteen healthy male participants performed 3-step layups and drop landings using their dominant leg on force plates. The fatigue protocol consisted of squat jumps, step-ups, and repeated countermovement jumps (CMJs), with fatigue defined as three consecutive CMJs below 80% of the participant’s pre-established maximum jump height. After a fatigue protocol, they repeated the tasks. Kinematic data were collected using an eight-camera Vicon system (100 Hz), and GRF data were recorded with two AMTI force plates (1000 Hz). Thirty-six reflective markers were placed on lower-limb anatomical landmarks, and data were processed using Visual 3D. Paired t-tests ($\alpha$ = 0.05) were conducted using SPSS (V26.0) to compare pre- and post-fatigue outcomes. Results: Significant increases were found in peak GRF during landing (pre: 3.41 $\pm$ 0.81 BW [Body Weight], post: 3.95 $\pm$ 1.05 BW, p = 0.036), and peak negative hip joint work during landing (pre: $-$0.34 $\pm$ 0.18 J/kg, post: $-$0.66 $\pm$ 0.43 J/kg, p = 0.025). Conclusions: These findings indicate that fatigue may alter landing mechanics, reflected in increased ground reaction forces and negative hip joint work. These preliminary findings should be interpreted cautiously, and future studies with larger samples and additional neuromuscular measures under sport-specific conditions are needed to improve ecological validity. Full article

Background/Objectives: National Football League (NFL) American football players are exposed to osteoarthritis risk factors of obesity and high joint loads. We sought to examine the association between total body mass (TBM), lean body mass (LBM), body fat percentage (BF%), and normalized compressive knee joint reaction forces (JRFcomp), peak knee adductor moments (KAM), and vertical ground reaction forces (vGRF) in NFL draft-eligible players during a high-speed run. Methods: A total of 125 participants ran a single trial at 5.5–6.5 m/s for 5 s on an instrumented treadmill. Bilateral vGRF and knee joint kinetics were calculated using inverse dynamics. Body composition was assessed using bioelectrical impedance. Results: LBM demonstrated significant moderate associations with vGRF (left, r(123) = −0.56, p < 0.001; right, r(123) = −0.60, p < 0.001) and low-to-negligible associations with KAM (left, r(123) = −0.20, p = 0.026; right, r(123) = −0.30, p < 0.001) and JRFcomp (left, r(123) = −0.39, p = 0.020; right, r(123) = −0.38, p = 0.015), respectively. TBM showed significant moderate negative associations with vGRF (left, r(123) = −0.56, p < 0.001; right, r(123) = −0.61, p < 0.001) and low-to-negligible associations with KAM (left, r(123) = −0.21, p = 0.021; right, r(123) = −0.28, p = 0.002) and JRFcomp (left, r(123) = −0.39, p < 0.001; right, r(123) = −0.37, p < 0.001), respectively. BF% showed significant low-to-negligible negative associations with JRFcomp (left, r(123) = −0.21, p < 0.001; right, r(123) = −0.22, p < 0.001) and vGRF (left, r(123) = −0.39, p < 0.001; right, r(123) = −0.41, p < 0.001), respectively, and no significant associations with KAM, p > 0.05. The heavier group exhibited significantly lower normalized JRFcomp, and vGRF, p < 0.05. Conclusions: Heavier, but not fatter, players attenuate knee loads. Dampening may be a short-term protective strategy for joints of heavier players. Full article

Arch structure is a crucial interface between the human body and the ground during landing tasks, but the biomechanical effects of arch support stiffness remain insufficiently explored. This study examines the effects of arch supports with different stiffnesses on lower-limb biomechanics during landing. Twelve male participants (six normal arches, six flat feet) performed a single-leg drop landing from a 45 cm height under four arch support conditions: no arch support pad (NAP), soft-stiffness arch support pad (SAP), medium-stiffness arch support pad (MAP), and high-stiffness arch support pad (HAP). Dominant lower-limb joint angles and moments in the sagittal plane and vertical ground reaction force (vGRF)-related parameters—time to peak vGRF, peak vGRF, and max loading rate—were recorded using a motion capture system and force plate. Data were analyzed using one-way repeated measures analysis of variance (ANOVA). Arch pad stiffness significantly affected ankle and knee kinematics. The NAP condition exhibited significantly higher ankle plantarflexion at initial contact (p ≤ 0.01), as well as larger range of motion (ROM) of the knee (p = 0.03) and hip (p < 0.01), compared to the use of a SAP or MAP. The use of a HAP resulted in a significantly lower peak ankle dorsiflexion moment and larger peak knee flexion angle than the other conditions (p ≤ 0.04). The peak knee extension moment was the highest when using a NAP, and was significantly higher than that shown with the use of a MAP or HAP (p ≤ 0.02). No significant differences were observed in hip joint moments or vGRF-related parameters across conditions (p ≥ 0.52). These results indicate that hard-stiffness arch support pads modulate lower-limb mechanics during landing, potentially enhancing shock absorption and reducing knee loading. Full article

Background/Objectives: Early diagnosis of Parkinson’s Disease (PD) is essential for initiating interventions that may slow its progression and enhance patient quality of life. Gait analysis provides a non-invasive means of capturing subtle motor disturbances, enabling the prediction of both disease presence and severity. This study evaluates and contrasts Bayesian-optimized convolutional neural network (CNN) and long short-term memory (LSTM) models applied directly to Vertical Ground Reaction Force (VGRF) signals for Parkinson’s disease detection and staging. Methods: VGRF recordings were segmented into fixed-length windows of 5, 10, 15, 20, and 25 s. Each segment was normalized and supplied as input to CNN and LSTM network. Hyperparameters for both architectures were optimized via Bayesian optimization using five-fold cross-validation. Results: The Bayesian-optimized LSTM achieved a peak binary classification accuracy of 99.42% with an AUC of 1.000 for PD versus control at the 10-s window, and 98.24% accuracy with an AUC of 0.999 for Hoehn–Yahr (HY) staging at the 5-s window. The CNN model reached up to 98.46% accuracy (AUC = 0.998) for binary classification and 96.62% accuracy (AUC = 0.998) for multi-class severity assessment. Conclusions: Bayesian-optimized CNN and LSTM models trained on VGRF data both achieved high accuracy in Parkinson’s disease detection and staging, with the LSTM exhibiting a slight edge in capturing temporal patterns while the CNN delivered comparable performance with reduced computational demands. These results underscore the promise of end-to-end deep learning for non-invasive, gait-based assessment in Parkinson’s disease. 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 response to perturbations in the gait of elderly and young individuals can differ due to various factors, such as age-related changes in sensorimotor function, muscle strength, and balance control. This study aimed to identify and compare compensatory kinematic and kinetic gait strategies in response to sudden treadmill perturbations applied during the Pre-Swing phase in young and older adults. The analysis focused on determining age-related differences in joint behavior and force production under perturbation stress, with implications for fall prevention. Twenty-one young and an equal number of elderly healthy females walked on a treadmill in a virtual environment (GRAIL, Motek). Unexpected perturbations were applied five times. Principal Component Analysis (PCA) and k-means clustering identified three distinct compensatory strategies per limb. Young adults primarily employed Strategies I (42.2%) and II (40%), while older adults most often selected Strategy II (45.5%). Statistical analysis (SPM and Mann-Whitney U test, p = 0.05) showed significant between-group differences in joint angles and torques across the gait cycle. For instance, in Strategy I, young participants had significantly lower ankle plantarflexion angles (p < 0.01) and hip extension torques (p < 0.05) compared to the elderly. Strategy II in older adults showed significantly higher vGRF minimums (p < 0.01) and anterior-posterior GRF peaks (p < 0.001). The elderly adopted strategies compatible with their neuromuscular capacity rather than those minimizing joint load, as observed in the young group. These findings offer novel insights into age-related compensatory mechanisms and highlight the importance of tailored fall-prevention strategies based on biomechanical response patterns. 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

Foot hyperpronation is a common anatomical misalignment that may contribute to the development of both localized and distant musculoskeletal overuse injuries. Advancements in modern technology may enable the detection of biomechanical changes in dynamic conditions that cannot be captured through conventional foot alignment assessments. This study aimed to investigate potential differences in spatiotemporal, dynamic, and center of pressure (COP)-related gait parameters, between individuals with foot hyperpronation (n = 21) and those with a neutral foot type (n = 21) under various walking conditions, using an instrumented treadmill. These conditions included walking downhill at −20% slope at 3.5 km·h⁻¹, and at −10% slope at 5.0 km·h⁻¹, level (0%) at 5.0 km·h⁻¹, and uphill at +10% slope at 3.5 km·h⁻¹ and +20% slope at 2.5 km·h⁻¹, each lasting five minutes. The results showed no significant differences in stride length and time, foot rotation, step width, cadence, or gait phase durations between the two groups. However, individuals with hyperpronated feet exhibited a more forward and mediolaterally displaced COP, higher vertical ground reaction forces (vGRFs) at the midfoot, and lower vGRFs at the lateral forefoot. Instrumented treadmills enable clinicians and sports scientists to detect specific traits in individuals with foot hyperpronation, which would otherwise go undetected through static assessments. Full article

This study aimed to assess the effect of real-time vertical ground reaction force (VGRF) biofeedback on sagittal plane hip and knee joint biomechanics and extensor muscle activities in older adults. Fifteen healthy older adults (71 ± 5.8 years) walked on a treadmill while instructed to increase their first peak of VGRF via biofeedback. Whole-body kinetic and kinematic data and electromyography data for the vastus lateralis and gluteus maximus muscles were recorded. A one-way repeated measure ANOVA followed by post hoc analysis was conducted. Results showed increases in peak VGRF (20.95%), knee extension torque (73.7%), knee flexion angle (53.8%), and vastus lateralis muscle activity (72.1%) during the loading response, with percentage changes calculated as the mean of acquisition and recall trials relative to baseline walking. In contrast, no significant effect on peak hip extension torque and hip flexion angle over time was observed. These findings suggest that biofeedback can induce greater vertical support forces production with increased knee extension torque and extensor muscle activity. In addition, older adults adopted higher vertical support without increasing hip joint torque, potentially aiding in mitigating age-related distal-to-proximal joint torque redistribution. These findings suggest that VGRF biofeedback could potentially be an effective intervention to enhance knee extensor activation and mobility in older adults without increasing hip load. Full article

Objective: Our objective was to investigate the biomechanical and neuromuscular adaptations of the lower limbs during the landing phase of running under fatigue conditions. Methods: A controlled fatigue protocol was used to induce running-related fatigue in participants. Data were collected using a three-dimensional motion capture system, force platform analysis, and surface electromyography (sEMG). Kinematic variables, such as hip, knee, and ankle joint angles and range of motion, were analyzed alongside kinetic parameters, including vertical ground reaction forces (vGRFs) and joint moments. sEMG was used to measure the muscle activation levels of the rectus femoris, biceps femoris, tibialis anterior, and gastrocnemius, and to calculate antagonist coactivation ratios. Statistical analyses were performed to assess the differences in pre- and post-fatigue using paired t-tests, with a significance level set at α = 0.05, and FDR correction was applied to control for multiple comparisons. Results: Post-fatigue, hip and knee flexion angles at initial contact decreased by 4.5% and 4.8%, respectively (FDR-adjusted p = 0.023, 0.0157), while their range of motion increased significantly by 10.4% and 11.1% (FDR-adjusted p = 0.0115, 0.0063). The second vGRF peak increased by 2.1% post-fatigue (FDR-adjusted p = 0.0086), with no significant changes in the first vGRF peak (p > 0.05). Muscle activation levels significantly increased in the rectus femoris (10.7%), biceps femoris (8.3%), tibialis anterior (9.1%), and gastrocnemius (10.2%) (FDR-adjusted p < 0.05). The antagonist coactivation ratio significantly decreased in the early and late landing phases (FDR-adjusted p = 0.0033, 0.0057), reflecting neuromuscular adjustments to fatigue. Conclusions: Fatigue-induced adaptations in joint kinematics, muscle activation, and coactivation strategies optimize performance and stability but may increase mechanical stress on lower-limb joints, highlighting a need for targeted interventions to mitigate injury risk. Full article

Background/Objectives: Eccentric loading during landing is considered a key factor in the development of patellar tendinopathy and is associated with stiff landings and patellar tendinopathy. This study aims to investigate the relationship between tendon structure, presence of pain, and sex differences in landing kinematics and kinetics during countermovement jumps (CMJ) and tuck jump tests (TJT) in professional volleyball players. Methods: Professional volleyball players aged 18 to 30 years old (14 females and 25 males) participated in a cross-sectional study. Data included the Victorian Institute of Sport Assessment Patellar Tendon (VISA-P) questionnaire; patellar tendon ultrasound characterization tissue (UTC) scans, in order to identify groups with misaligned tendon fibers (MTF) or aligned tendon fibers (ATF); and biomechanical assessments for CMJ and TJT. The joint angle (JA) at the lower limb was measured at peak ground reaction force (peak_vGRF) and maximal knee flexion (max_KF). A general linear model was used to evaluate joint JA differences between tendon alignment, pain, and sex variables. Sample t-tests compared peak_vGRF, load time, load rate, and area based on tendon alignment, pain presence, sex, and jump. The statistical significance of p-value is >0.05, and the effect size (ES) was also calculated. Results: The MTF group revealed decreased knee JA during TJT at peak_vGRF (p = 0.01; ES = −0.66) and max_KF (p = 0.02; ES = −0.23). The presence of pain was associated with increased JA during the CMJ, particularly at peak_vGRF and max_KF for trunk, hip, and ankle joints. Females showed decreased peak_vGRF than males. Landing with misaligned tendon fibers showed longer load times compared to aligned tendon fibers (p = 0.021; ES = −0.80). The TJT exhibited a greater load rate than the CMJ (p = 0.00; ES = −0.62). Conclusions: Pain is a critical factor influencing greater JA during landing, particularly at the trunk, hip, and ankle joints in CMJ. Misaligned tendon fibers compromise landing dynamics by increasing trunk JA during TJT. Kinetics varied significantly by sex and jump type, while pain and tendon structure revealed limited differences. Full article