Continuous time series analysis on the effects of induced running fatigue on leg symmetry using kinematics and kinetic variables: Implications for knee joint injury during a countermovement jump

Dataset of walking and running biomechanics with different step widths across different speeds

The mediolateral distance between both heels at first contact is known as step width, defined as a frontal plane spatial variable. Short-term variations in step width during walking and running may impact the lower-limb biomechanics in all three planes. Considering these features, the proposed dataset of this study was established on 13 healthy young males aged between 20 and 24 years within the normal BMI range, providing data in raw ready for use for the community. In laboratory conditions, participants were required to locomote (walk and run) at six different step widths while walking at a preferred speed, running at 3.0 m/s, and running at 3.7 m/s. This dataset could expand the population sample size of similar relevant datasets and provide the data basis for future exploring on the effect of acute step width changes on the kinematic and kinetic chain in human lower at different movement speeds.

Lower limbs kinematic analysis during a jump landing task in soccer players with unilateral anterior cruciate ligament reconstruction

BACKGROUND

Fatigue leads to an acute decline in muscle strength, altered patterns of lower extremity muscle activation, changes in hip and knee kinematics. In terms of the effects of fatigue on knee joint kinematics during plyometric training, there is still a lack of knowledge regarding kinematic differences between athletes who passed the ACL reconstructions rehabilitation period and healthy athletes. Therefore, this study aimed to compare lower limb joint kinematic parameters between reconstructed cruciate ligament and healthy control soccer players during jump landing in a fatigued setting.

METHODS

Lower limb kinematic parameters were recorded in 20 professional soccer players (age, 24.95 ± 2.92 years; body mass, 77.20 ± 12.88 kg; height, 1.77 ± 3.19 m) during jump landing task before and after the fatigue protocol. The control group consisted of healthy subjects and the experimental group consisted of subjects with ACL reconstruction by thigh transplantation. Kinematic data was recorded with 4 cameras to measure lower limb angles at first foot contact and maximum range of motion.

RESULTS

The results showed that before fatigue, there was only a significant difference between the two groups in the maximum range of motion of the non-involved hip joint (P = 0.022) and angle of the involved hip at first contact (P = 0.049). In other data on joint range of motion or initial contact angle, no significant difference was observed between the two groups (P > 0.05). After fatigue protocol, there was a significant difference in initial foot contact in non-involved (P = 0.030), and involved (P = 0.020) hip joint angles between the two groups. However, no significant difference in initial contact angle or range of motion of other joints was observed between the groups.

CONCLUSIONS

This study shows that plyometric fatigue does not contribute to numerous changes in contact angles and range of motion in lower extremity joints in healthy soccer players and those with a history of cruciate ligament repairs.

Robust fatigue markers obtained from muscle synergy analysis

This study aimed to utilize the nonnegative matrix factorization (NNMF) algorithm for muscle synergy analysis, extracting synergy structures and muscle weightings and mining biomarkers reflecting changes in muscle fatigue from these synergy structures. A leg press exercise to induce fatigue was performed by 11 participants. Surface electromyography (sEMG) data from seven muscles, electrocardiography (ECG) data, Borg CR-10 scale scores, and the z-axis acceleration of the weight block were simultaneously collected. Three indices were derived from the synergy structures: activation phase difference, coactivation area, and coactivation time. The indicators were further validated for single-leg landing. Differences in heart rate (HR) and heart rate variability (HRV) were observed across different fatigue levels, with varying degrees of disparity. The median frequency (MDF) exhibited a consistent decline in the primary working muscle groups. Significant differences were noted in activation phase difference, coactivation area, and coactivation time before and after fatigue onset. Moreover, a significant correlation was found between the activation phase difference and the coactivation area with fatigue intensity. The further application of single-leg landing demonstrated the effectiveness of the coactivation area. These indices can serve as biomarkers reflecting simultaneous alterations in the central nervous system and muscle activity post-exertion.

Residual coronary malformation after tibial shaft fracture alters the contact status of the meniscus and cartilage in the knee joint: a computational study

Objective

The purpose of this study was to evaluate the effect of residual varus/valgus deformity on the mechanical characteristics of the meniscus and cartilage after tibial shaft fracture.

Methods

A finite element model of the lower extremity of a healthy volunteer was constructed from CT and MRI images. The upper and middle tibial fracture models were modified to produce 3°, 5°, and 10° tibial varus/valgus models. For model validation, a patient-specific model with a 10° tibial varus deformity was constructed and simulated under the same boundary conditions.

Results

The contact area and maximum stress of the normal and modified deformity models were similar to those of the reported studies and a patient-specific model. The maximum stress, contact area, and contact force of the medial tibial cartilage in a normal neutral position were 0.64 MPa, 247.52 mm2, and 221.77 N, respectively, while those of the lateral tibial cartilage were 0.76 MPa, 196.25 mm2, and 146.12 N, respectively. From 10° of valgus to 10° of varus, the contact force, contact area, and maximum stress values of the medial tibial cartilage increased, and those of the lateral tibial cartilage gradually decreased. The maximum stress, contact area, and contact force of the medial tibial cartilage in the normal neutral position were 3.24 MPa, 110.91 mm2, and 62.84 N, respectively, while those of the lateral tibial cartilage were 3.45 MPa, 135.83 mm2, and 67.62 N, respectively. The maximum stress of the medial tibial subchondral bone in a normal neutral position was 1.47 MPa, while that of the lateral was 0.65 MPa. The variation trend of the medial/lateral meniscus and subchondral bone was consistent with that of the tibial plateau cartilage in terms of maximum stress, contact area, and contact force.

Conclusion

The residual varus/valgus deformity of the tibia has a significant impact on the mechanical loads exerted on the knee joint. This study provides a mechanical basis and references for the clinical evaluation of tibial fracture reduction and osteotomy for tibial deformity.

Differences in lower-limb biomechanics during single-leg landing considering two peripheral fatigue tasks

Dynamic knee valgus (DKV) occurs during landing after a fatigue task involving the lower extremity. However, the manner in which different peripheral fatigue tasks affect DKV remains unknown. In this study, we investigated the DKV via electromyography during single-leg landing considering the hip-joint fatigue task (HFT) and knee-joint fatigue task (KFT) performed by healthy men. We recruited 16 healthy male participants who performed a single-leg jump-landing motion from a height of 20 cm before and after an isokinetic hip abduction/adduction task (HFT) and knee extension/flexion task (KFT). Three-dimensional motion analysis systems were attached to the left gluteus medius and quadriceps, and surface electromyography was used to analyze the lower limb kinematics, kinetics, and muscle activity. The primary effects and interactions of the task and fatigue were identified based on the two-way repeated-measures analysis of variance. The results of the average angle during landing indicated that DKV occurs in KFT, whereas HFT applies external forces that adduct and internally rotate the knee at peak vertical ground reaction force (vGRF). Furthermore, both KFT and HFT exhibited an increase in muscle activity in the quadriceps. The analysis revealed that the occurrence of DKV varies depending on the peripheral fatigue task, and the effects on average DKV during landing and DKV at peak vGRF vary depending on the peripheral fatigue task.

Automated recognition of asymmetric gait and fatigue gait using ground reaction force data

Introduction: The purpose of this study was to evaluate the effect of running-induced fatigue on the characteristic asymmetry of running gait and to identify non-linear differences in bilateral lower limbs and fatigued gait by building a machine learning model. Methods: Data on bilateral lower limb three-dimensional ground reaction forces were collected from 14 male amateur runners before and after a running-induced fatigue experiment. The symmetry function (SF) was used to assess the degree of symmetry of running gait. Statistical parameter mapping (Paired sample T-test) algorithm was used to examine bilateral lower limb differences and asymmetry changes pre- and post-fatigue of time series data. The support vector ma-chine (SVM) algorithm was used to recognize the gait characteristics of both lower limbs before and after fatigue and to build the optimal algorithm model by setting different kernel functions. Results: The results showed that the ground reaction forces were asymmetrical (SF > 0.5) both pre-and post-fatigue and mainly concentrated in the medial-lateral direction. The asymmetry of the medial-lateral direction increased significantly after fatigue (p < 0.05). In addition, we concluded that the polynomial kernel function could make the SVM model the most accurate in classifying left and right gait features (accuracy of 85.3%, 82.4%, and 82.4% in medial-lateral, anterior-posterior and vertical directions, respectively). Gaussian radial basis kernel function was the optimal kernel function of the SVM algorithm model for fatigue gait recognition in the medial-lateral and vertical directions (accuracy of 54.2% and 62.5%, respectively). Moreover, polynomial was the optimal kernel function of the anterior-posterior di-rection (accuracy = 54.2%). Discussion: We proved in this study that the SVM algorithm model depicted good performance in identifying asymmetric and fatigue gaits. These findings can provide implications for running injury prevention, movement monitoring, and gait assessment.