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Introduction: Ballet demands diverse physical prowess, requiring dancers to execute movements symmetrically, irrespective of their dominant leg. Ballet often includes jumps, including the basic open-leg leap-the grand jeté-which requires uniform performance regardless of the leg on which the leap is initiated. However, no studies have simultaneously evaluated the effects of dominant leg or movement variation on jump height, leg split angle, jump time, and floor reaction forces during take-off and landing, which are related to the feeling of floating in the grand jeté. This study aimed to determine whether the high-level, stable, and beautiful performance required of professional ballet dancers in the grand jeté is affected by the dominant leg. Methods: Twelve female ballet dancers, all right leg dominant, performed the grand jeté 3 times on each side, distinguishing between dominant (right leg landing) and non-dominant (left leg landing) grand jetés. Utilising 3D movement analysis, we measured jump height, jump time, maximum leg split angle, and maximum vertical ground reaction force (VGRF) during take-off and landing. Mean values and coefficients of variation were calculated for each analysed parameter. Paired sample t-tests were conducted to assess differences between left and right grand jetés, with a significance level set at P < .05. Results: Statistically significant differences were observed in jump height (P = .028) and jump time (P = .001) when comparing the average of three trials for each side. However, no significant differences were found in maximum leg split angle (P = 0.643), maximum VGRF at take-off (P = .200), and maximum VGRF at landing (P = .109). In addition, no significant differences in coefficients of variation were identified for all items. Conclusion: Ballet dancers showed consistent performance on dominant and non-dominant legs but higher and longer jumps for grand jetés landing on the dominant leg, which may have affected overall performance.

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PURPOSE: Jump and linear sprint performances both correlate with pro-agility performance. However, correlation does not imply causation, and potential confounders may affect the correlation. Therefore, this study aimed to determine the relationship between change-of-direction (COD) performance and COD deficits (COD-D) in linear-sprint and countermovement-jump (CMJ) -related performance using multiple stepwise linear-regression models. METHODS: The study included 42 female national-level intercollegiate athletes. The 10- and 20-m linear-sprint and pro-agility times, COD-D, CMJ height, and phase-specific force production and rate of force development during eccentric unloading, eccentric braking, and the concentric phases of CMJ were measured. Stepwise linear-regression analyses were used to predict the factors related to COD and COD-D. RESULTS: CMJ height was the sole predictor in the 10-m pro-agility model (adjusted R2 = .234, P = .001). Modified Reactive Strength Index (standardized coefficient, -.710) and the lowest center-of-mass depth during the CMJ (standardized coefficient, .323) were predictors in the 20-m pro-agility model (adjusted R2 = .330, P < .001). For the 10- and 20-m COD-D models, the rate of force development at 30 and 60 milliseconds, respectively, during the concentric phase was the only predictor of performance (adjusted R2 = .183, P = .003 and .237, P = .001, respectively). CONCLUSIONS: These results suggest that athletes should concentrate on improving their CMJ height, increasing their ability to lower their center of mass more deeply, and increasing their instantaneous force-production abilities immediately after the eccentric braking phase of CMJ to improve their COD performance.

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Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.

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CONTEXT: Slower habitual walking speed and aberrant gait biomechanics are linked to clinically significant knee-related symptoms and articular cartilage composition changes linked to posttraumatic osteoarthritis (PTOA) following anterior cruciate ligament reconstruction (ACLR). OBJECTIVE: To determine specific gait biomechanical variables that can accurately identify individuals with clinically significant knee-related symptoms post-ACLR, and the corresponding threshold values, sensitivity, specificity, and odds ratios for each biomechanical variable. DESIGN: Cross-sectional analysis. SETTING: Laboratory. PATIENTS OR OTHER PARTICIPANTS: Seventy-one individuals (n=38 female; age=21±4 years; height=1.76±0.11 m; mass=75.38±13.79 kg) who were 6 months post-primary unilateral ACLR (6.2±0.4 months). MAIN OUTCOME MEASURES: 3D motion capture of 5 overground walking trials was used to calculate discrete gait biomechanical variables of interest during stance phase (1st and 2nd peak vertical ground reaction force [vGRF]; midstance minimum vGRF; peak internal knee abduction and extension moments; and peak knee flexion angle), along with habitual walking speed. Knee Injury and Osteoarthritis Outcome Scores (KOOS) was used to dichotomize patients as symptomatic (n=51) or asymptomatic (n=20) using the Englund et al. 2003 KOOS guidelines for defining clinically significant knee-related symptoms. Separate receiver operating characteristic (ROC) curves and respective areas under the curve (AUC) were used to evaluate the capability of each biomechanical variable of interest for identifying individuals with clinically significant knee-related symptoms. RESULTS: Habitual walking speed (AUC=0.66), vGRF at midstance (AUC=0.69), and 2nd peak vGRF (AUC=0.76), demonstrated low-to-moderate accuracy for identifying individuals with clinically significant knee-related symptoms. Individuals who exhibited habitual walking speeds ≤1.27 m/s, midstance vGRF ≥0.82 BW, and 2nd peak vGRF ≤1.11 BW, demonstrated 3.13, 6.36, and 9.57 times higher odds of experiencing clinically significant knee-related symptoms, respectively. CONCLUSIONS: Critical thresholds for gait variables may be utilized to identify individuals with increased odds of clinically significant knee-related symptoms and potential targets for future interventions.

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Parkinson's disease (PD) is one of the cognitive degenerative disorders of the central nervous system that affects the motor system. Gait dysfunction represents the pathology of motor symptom while gait analysis provides clinicians with subclinical information reflecting subtle differences between PD patients and healthy controls (HCs). Currently neurologists usually assess several clinical manifestations of the PD patients and rate the severity level according to some established criteria. This is highly dependent on clinician's expertise which is subjective and ineffective. In the present study we address these issues by proposing a hybrid signal processing and machine learning based gait classification system for gait anomaly detection and severity rating of PD patients. Time series of vertical ground reaction force (VGRF) data are utilized to represent discriminant gait information. First, phase space of the VGRF is reconstructed, in which the properties associated with the nonlinear gait system dynamics are preserved. Then Shannon energy is used to extract the characteristic envelope of the phase space signal. Third, Shannon energy envelope is decomposed into high and low resonance components using dual Q-factor signal decomposition derived from tunable Q-factor wavelet transform. Note that the high Q-factor component consists largely of sustained oscillatory behavior, while the low Q-factor component consists largely of transients and oscillations that are not sustained. Fourth, variational mode decomposition is employed to decompose high and low resonance components into different intrinsic modes and provide representative features. Finally features are fed to five different types of machine learning based classifiers for the anomaly detection and severity rating of PD patients based on Hohen and Yahr (HY) scale. The effectiveness of this strategy is verified using a Physionet gait database consisting of 93 idiopathic PD patients and 73 age-matched asymptomatic HCs. When evaluated with 10-fold cross-validation method for early PD detection and severity rating, the highest classification accuracy is reported to be 98.20% and 96.69%, respectively, by using the support vector machine classifier. Compared with other state-of-the-art methods, the results demonstrate superior performance and support the validity of the proposed method.

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BACKGROUND: Ankle sprains are common and cause persistent ankle function reduction. To biomechanically evaluate the ankle function after ankle sprains, the ground reaction force (GRF) measurement during the single-legged landing had been used. However, previous studies focused on discrete features of vertical GRF (vGRF), which largely ignored vGRF waveform features that could better identify the ankle function. PURPOSE: To identify how the history of ankle sprain affect the vGRF waveform during the single-legged landing with unsupervised machine learning considering the time-series information of vGRF. METHODS: Eighty-seven currently healthy basketball athletes (12 athletes without ankle sprain, 49 athletes with bilateral, and 26 athletes with unilateral ankle sprain more than 6 months before the test day) performed single-legged landings from a 20 centimeters (cm) high box onto the force platform. Totally 518 trials vGRF data were collected from 87 athletes of 174 ankles, including 259 ankle sprain trials (from previous sprain ankles) and 259 non-ankle sprain trials (from without sprain ankles). The first 100 milliseconds (ms) vGRF waveforms after landing were extracted. Principal component analysis (PCA) was applied to the vGRF data, selecting 8 principal components (PCs) representing 96% of the information. Based on these 8 PCs, k-means method (k = 3) clustered the 518 trials into three clusters. Chi-square test assessed significant differences (p < 0.01) in the distribution of ankle sprain and non-ankle sprain trials among clusters. FINDINGS: The ankle sprain trials accounted for a significantly larger percentage (63.9%) in Cluster 3, which exhibited rapidly increased impulse vGRF waveforms with larger peaks in a short time. SIGNIFICANCE: PCA and k-means method for vGRF waveforms during single-legged landing identified that the history of previous ankle sprains caused a loss of ankle absorption ability lasting at least 6 months from an ankle sprain.

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BACKGROUND: As people age there is a proximal shift of joint moment generation from ankle plantarflexion and knee extension toward hip extension and flexion moments. This age-related redistribution has been documented in the context of propulsive force generation during the push-off phase with less evidence in the context of weight bearing. Additionally, these sagittal plane joint moments have been a primary focus of studies though the hip frontal plane moment also contributes to vertical support but has received less attention. Furthermore, how aging affects the relationships between changes in sagittal and frontal joint moments and changes in vertical support force as a function of walking speed remains unclear RESEARCH QUESTION: How does aging affect the contributions of sagittal and frontal plane joint moments to weight-bearing across different walking speeds? METHODS: Gait analysis was performed on 24 young and 17 healthy older subjects walked on the treadmill at their preferred and 30 % faster speeds. Stepwise linear regression analysis was performed to determine the joint moments that predict the peak amplitudes of the vertical ground reaction force (VGRF) across different walking speeds. RESULTS: Hip abduction and knee extension moments were the primary contributors to leading limb weight-bearing in young, whereas hip extension moment was the primary contributor in older adults. Ankle plantarflexion moment was the main contributor to trailing limb weight-bearing in young and hip flexion moment was the main contributor in older adults. From preferred to faster walking speed changes in knee extension moment were the primary contributor to changes in the trailing limb weight-bearing in young whereas changes in hip extension moment were the primary contributor in olderadults. SIGNIFICANCE: These findings suggested that older and younger adults used different joint moment contributions to produce leading limb and trailing limb vertical support forces across different walking speeds.

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One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy.

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Introduction: Parkinson's disease (PD) is characterized by an alteration of the walking gait, frequently including a slower self-selected walking speed (SSWS). Although the reduction of walking speed is inherent to people with PD, such speed reduction also represents a potential confounding factor that might partly explain the observed gait differences between PD and control participants. Methods: In this study, each participant walked along a 25 m level corridor during which vertical ground reaction force signals were recorded using shoes equipped with eight pressure sensors. Vertical ground reaction force signals (using statistical parametric mapping) and temporal and kinetic variables as well as their related variability and asymmetry (using Student's t-test) were compared between PD (n = 54) and walking-speed-matched control subjects (n = 39). Results: Statistical parametric mapping did not yield significant differences between PD and control groups for the vertical ground reaction force signal along the walking stance phase. Stride time and single support time (equivalent to swing time) were shorter and peak vertical ground reaction force was larger in PD patients compared to controls (p ≤ 0.05). However, the single support time was no longer different between people with PD and healthy subjects when expressed relatively to stride time (p = 0.07). While single support, double support, and stance times were significantly more variable and asymmetric for PD than for the control group (p ≤ 0.05), stride time was similar (p ≥ 0.07). Discussion: These results indicate that at matched SSWS, PD patients adopt a higher cadence than control participants. Moreover, the temporal subdivision of the walking gait of people with PD is similar to healthy individuals but the coordination during the double support phase is different. Hence, this study indicates that isolating the speed factor is crucial in gait analysis for PD.

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Objective A practical and highly accurate algorithm for dynamic monitoring of plantar pressure was proposed, the magnitude of vertical ground reaction force (vGRF) during walking was measured by a capacitive insole sensor, and reliability of the prediction accuracy was verified. Methods Four healthy male subjects were require to wear capacitive insole sensors, and their fast walking and slow walking data were collected by Kistler three-dimensional (3D) force platform. The data collected by the capacitive insole sensors were pixelated, and then the processed data were fed into a residual neural network, ResNet18, to obtain high-precision vGRF. Results Compared with analysis of the data collected from Kister force platform, the normalized root mean square error (NRMSE) for fast walking and slow walking were 8.40% and 6.54%, respectively, and the Pearman correlation coefficient was larger than 0.96. Conclusions This study provides a novel algorithm for dynamic measurement of GRF in mobile scenarios, which can be used for estimation of complete GRF outside the laboratory without being constrained by the number and location of force plates. Potential application areas include gait analysis and efficient capture of pathological gaits.

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BACKGROUND: In recent years the use of sample entropy (SampEn) to evaluate the complexity of the locomotor system in human gait data has gained in popularity. However, it has been suggested that SampEn is sensitive to various input parameters and signal preprocessing methods. This study quantified the effects of different temporal and spatial normalization approaches and various lengths of the template vector (m) on SampEn calculations. The discriminatory ability of SampEn was studied by comparing two walking conditions. METHODS: Twenty-three participants (seven males, 55.7 ± 8.5 years, 165.7 ± 7.9 cm, 80.5 ± 16.7 kg) walked on a treadmill with preferred (Vpref) and maximum (Vmax) speed. Data were segmented and resampled (SEGM), resampled and spatially normalized (NORM), resampled and detrended (ZERO). RESULTS: For vertical ground reaction force (vGRF) and center of pressure in anterio-posterior direction (COPap), in both walking conditions, SampEn was generally sensitive to the vector length and not to the data processing, except for COPap in ZERO, m = 2, 4. For the COPml SampEn behaved oppositely, it was sensitive to preprocessing method and not to the m length. The regularity of COPap and vGRF in all processed signals increased in Vmax condition. For the COPml only two signals, WHOLE and ZERO, revealed increased complexity caused by more demanding walking conditions. CONCLUSIONS: SampEn was able to discriminate between different walking conditions in all analyzed variables, but not in all signals. Depending on evaluated variable, SampEn was susceptible in different way for the m level and processing method. Hence, these should be checked and selected for each variable independently. For future studies evaluating influence of walking velocity on COP and vGRF regularity during treadmill walking it is advised to use raw time series. Furthermore, to maintain template vector which represents biological relevance it is advised to detect highest frequencies present in analyzed signals and evaluate minimal time interval which can reflect change caused by response of a neuromuscular system. During evaluating treadmill walking measured with 100 Hz sampling frequency it is recommended to adopt m from 6 to 10, when average stride time is up to about 1 s.

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When a person stands upright quietly, the position of the Centre of Mass (CoM), the vertical force acting on the ground and the geometrical configuration of body segments is accurately controlled around to the direction of gravity by multiple feedback mechanisms and by integrative brain centres that coordinate multi-joint movements. This is not always easy and the postural muscles continuously produce appropriate torques, recorded as ground reaction force by a force platform. We studied 23 young adults during a 90 s period, standing at ease on a hard (Solid) and on a compliant support (Foam) with eyes open (EO) and with eyes closed (EC), focusing on the vertical component of the ground reaction force (VGRF). Analysis of VGRF time series gave the amplitude of their rhythmic oscillations (the root mean square, RMS) and of their frequency spectrum. Sway Area and Path Length of the Centre of Pressure (CoP) were also calculated. VGRF RMS (as well as CoP sway measures) increased in the order EO Solid ≈ EC Solid < EO Foam < EC Foam. The VGRF frequency spectra featured prevailing frequencies around 4-5 Hz under all tested conditions, slightly higher on Solid than Foam support. Around that value, the VGRF frequencies varied in a larger range on hard than on compliant support. Sway Area and Path Length were inversely related to the prevailing VGRF frequency. Vision compared to no-vision decreased Sway Area and Path Length and VGRF RMS on Foam support. However, no significant effect of vision was found on VGRF mean frequency for either base of support condition. A description of the VGRF, at the interface between balance control mechanisms and sway of the CoP, can contribute information on how upright balance is maintained. Analysis of the frequency pattern of VGRF oscillations and its role in the maintenance of upright stance should complement the traditional measures of CoP excursions in the horizontal plane.

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Increased vertical ground reaction force (vGRF) and dynamic knee valgus contribute to non-contact anterior cruciate ligament (ACL) injuries. We examined feedback's influence during landing and transfer to a game-specific drill, measured by deceleration. Thirty-one female athletes performed 30 drop landings with augmented feedback and dual-task conditions, with a game-specific drill before and after. Differences were shown across time (baseline, feedback, post-feedback) and between conditions (with or without dual-task) in peak vGRF and knee to ankle ratio (K:A ratio). K:A ratio is the ratio of the frontal plane distance between the knees relative to the frontal plane distance between the ankles. This measure serves as a surrogate for knee valgus where a ratio closer to 1 indicates less knee valgus. There were reductions in peak vGRF (p < 0.05) and improvements in K:A ratio (p < 0.05) across time, improvements in K:A ratio across time and by condition (p < 0.05), and reduction in deceleration during landing in a game-specific drill (p < 0.05). Feedback may improve landing mechanics and transfer to a game-specific drill that can influence ACL injury in sport.

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OBJECTIVES: The purpose was to compare vertical ground reaction forces and sagittal-plane energy dissipation patterns of the lower extremity during a single-limb jump-stabilization task between individuals with chronic ankle instability who did or did not attend rehabilitation after their initial ankle sprain. DESIGN: Cross-sectional. SETTING: Research laboratory. PARTICIPANTS: Eight participants with chronic ankle instability who did and 12 participants who did not attend rehabilitation were enrolled. MAIN OUTCOME MEASURES: Normalized vertical ground reaction force data were used to calculate the average loading rate, time to peak force, and the peak force. Sagittal plane kinematics and joint moments at the ankle, knee, and hip, were used to calculate each joint's relative energy dissipation at 50, 100, 150, and 200 ms post-landing. RESULTS: Participants who attended rehabilitation had a slower average loading rate (P = 0.025) and smaller peak vertical ground reaction force (P = 0.025). The average relative energy dissipation at the knee was higher in those who attended rehabilitation at the 100 ms (P = 0.041), 150 ms (P = 0.046), and 200 ms (P = 0.042) time intervals. CONCLUSIONS: Attending rehabilitation after an ankle sprain may have a beneficial effect on jump-stabilization outcomes among individuals with chronic ankle instability.

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OBJECTIVE: A complex association exists between aberrant gait biomechanics and posttraumatic knee osteoarthritis (PTOA) development. Previous research has primarily focused on the link between peak loading during the loading phase of stance and joint tissue changes following anterior cruciate ligament reconstruction (ACLR). However, the associations between loading and cartilage composition at other portions of stance, including midstance and late stance, is unclear. The objective of this study was to explore associations between vertical ground reaction force (vGRF) at each 1% increment of stance phase and tibiofemoral articular cartilage magnetic resonance imaging (MRI) T1ρ relaxation times following ACLR. DESIGN: Twenty-three individuals (47.82% female, 22.1 ±4.1 years old) with unilateral ACLR participated in a gait assessment and T1ρ MRI collection at 12.25 ± 0.61 months post-ACLR. T1ρ relaxation times were calculated for the articular cartilage of the weightbearing medial and lateral femoral (MFC, LFC) and tibial (MTC, LTC) condyles. Separate bivariate, Pearson product moment correlation coefficients (r) were used to estimate strength of associations between T1ρ MRI relaxation times in the medial and lateral tibiofemoral articular cartilage with vGRF across the entire stance phase. RESULTS: Greater vGRF during midstance (46%-56% of stance phase) was associated with greater T1ρ MRI relaxation times in the MFC (r ranging between 0.43 and 0.46). CONCLUSIONS: Biomechanical gait profiles that include greater vGRF during midstance are associated with MRI estimates of lesser proteoglycan density in the MFC. Inability to unload the ACLR limb during midstance may be linked to joint tissue changes associated with PTOA development.

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Disruptions in knee biomechanics during walking following anterior cruciate ligament (ACL) injury have been suggested to lead to the development of premature knee osteoarthritis (OA) and to be potential markers of OA risk and targets for intervention. This study investigated if side-to-side differences in early stance peak vertical ground reaction force (vGRF) during walking 2 years after ACL reconstruction are associated with longer-term (10 years post-reconstruction) changes in patient-reported outcomes. Twenty-eight participants (mean age: 28.7 ± 6.4 years) with primary unilateral ACL reconstruction underwent gait analysis for assessment of peak vGRF and completed Knee Injury and Osteoarthritis Outcome Score (KOOS) and International Knee Documentation Committee (IKDC) surveys at 2 years post-surgery (2.2 ± 0.3 years) and completed surveys at follow-up 10 years post-surgery (10.5 ± 0.9 years). Associations between changes (10-2 years) in patient-reported outcomes and between limb-differences in vGRF were assessed with Pearson or Spearman's ρ correlation coefficients and exploratory backwards elimination multiple linear regression analyses. Differences in vGRF between symptomatic progressors and non-progressors were also assessed. The side-to-side difference in vGRF was related to the variability in longer-term changes in patient-reported outcome metrics and distinguished symptomatic progressors from non-progressors. Participants with higher vGRF in the reconstructed (ACLR) limb versus the contralateral limb had worsening of IKDC (R = -0.391, p = 0.040), KOOS pain (ρ = -0.396, p = 0.037), KOOS symptoms (ρ = -0.572, p = 0.001), and KOOS quality of life (R = -0.458, p = 0.014) scores at follow-up. Symptomatic progressors had greater vGRF in the ACLR limb as compared to the contralateral limb at baseline than non-progressors (p = 0.023). These data highlight associations between a simple-to-measure gait metric and the development of long-term clinical symptoms after an ACL injury.

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Mechanical factors have been implicated in the development of osteoarthritis after anterior cruciate ligament (ACL) reconstruction. This study tested for associations between ambulatory joint loading (total joint moment [TJM] and vertical ground reaction force [vGRF]) and changes in serum levels of cartilage oligomeric matrix protein (COMP) in response to a mechanical stimulus (30-min walk) in individuals with ACL reconstruction. Twenty-five subjects (mean age: 34.5 ± 9.8 years; 2.2 ± 0.2 years post-surgery) with primary unilateral ACL reconstruction underwent gait analysis for assessment of peak vGRF and TJM first (TJM1) and second (TJM2) peaks. Serum COMP concentrations were measured by enzyme-linked immunosorbent assay immediately before, 3.5 h, and 5.5 h after a 30-min walk. Pearson correlation coefficients and backward stepwise multiple linear regression analysis, with adjustments for age, sex, body mass index, and between-limb speed difference, assessed associations between changes in COMP and between-limb differences in joint loading parameters. Greater TJM1 (R = 0.542, p = 0.005), TJM2 (R = 0.460, p = 0.021), and vGRF (R = 0.577, p = 0.003) in the ACL-reconstructed limb as compared to the contralateral limb were associated with higher COMP values 3.5 h following the 30-min walk. Change in COMP at 5.5 h became a significant predictor of the between-limb difference in TJM1 and vGRF in multivariate analyses after accounting for the between-limb speed difference. These results demonstrate that higher TJM and vGRF in the ACLR limb as compared to the contralateral limb are associated with higher relative COMP levels 3.5 and 5.5 h after a 30-min walk. Future work should investigate the effect of therapies to alter joint loading on the biological response in individuals after ACL reconstruction.

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This paper presents an actuated spring-loaded inverted pendulum model with a vertically constrained suspended load mass to predict the vertical GRF and energetics of walking and running. Experiments were performed to validate the model prediction accuracy of vertical GRF. The average correlation coefficient was greater than 0.97 during walking and 0.98 during running. The model's predictions of energy cost reduction were compared with experimental data from the literature, and the difference between the experimental and predicted results was less than 7%. The predicted results of characteristic forces and energy cost under different suspension stiffness and damping conditions showed a tradeoff when selecting the suspension parameters of elastically suspended backpacks.

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Objective To establish the method of predicting the vertical ground reaction force (vGRF) during treadmill running based on principal component analysis and wavelet neural network (PCA-WNN). Methods Nine rearfoot strikers were selected and participated in running experiment on an instrumented treadmill at the speed of 12, 14 and 16 km/h. The kinematics data and vGRF were collected using infrared motion capture system and dynamometer treadmill. A three-layer neural network framework was constructed, in which the activation function of the hidden layers was the Morlet function. Velocities of mass center of the thigh, shank and foot as well as joint angles of the hip, knee and ankle were input into the WNN model. The prediction accuracy of the model was evaluated by the coefficient of multiple correlation (CMC) and error. The consistencies between predicted and measured peak GRF were analyzed by Bland-Altman method. Results The CMC between the predicted and measured GRF at different speeds were all greater than 0.99; the root mean square error (RMSE) between the predicted and measured vGRF was 0.18-0.28 BW; and the normalized root mean square error (NRMSE) was 6.20%-8.42%; the NRMSE between the predicted and measured impact forces and propulsive forces were all smaller than 15%. Bland-Altman results showed that the predicted peak errors of propulsive force at 12 km/h and that of impact force and propulsive force at 14 km/h were within the 95% agreement interval. Conclusions The PCA-WNN model constructed in this study can accurately predict the vGRF during treadmill running. The results provide a new method to obtain kinetic data and perform real-time monitoring on a treadmill, which is of great significance for studying running injuries and rehabilitation treatment.

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In this study, using vertical ground reaction force (VGRF) data and focusing on the stance phase of the gait cycle, the effect of Parkinson's disease (PD) on gait was investigated. The used dataset consisted of 93 PD and 72 healthy individuals. Multiple comparisons correction ANOVA test and student t-test were used for statistical analyses. Results showed that a longer stance duration with a larger VGRF peak value (p < 0.05) was observed for PD patients during the stance phase. In addition, the VGRF peak value was delayed and blunted in PD cases compared with healthy individuals. These results indicated more time and effort for PD patients for posture stabilization during the stance phase. The time delay for different locations of the foot sole to contact the ground during the stance phase indicated that PD patients might use a different strategy for maintaining their body stability compared with healthy individuals. Although the VGRF time-domain pattern during the stance phase in PD was similar to healthy conditions, its local characteristics like duration and peak value differed significantly. The classification analysis based on the VGRF time-domain extracted features during the stance phase obtained PD recognition with accuracy, sensitivity and specificity of 90.82%, 88.63% and 82.56%, respectively.

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