Do Novice Runners Show Greater Changes in Biomechanical Parameters?

Zhou

et al. 2021

Healthcare

Self Cite

Kinematics data are primary biomechanical parameters. A principal component analysis (PCA) of waveforms is a statistical approach used to explore patterns of variability in biomechanical curve datasets. Differences in experienced and recreational runners’ kinematic variables are still unclear. The purpose of the present study was to compare any differences in kinematics parameters for competitive runners and recreational runners using principal component analysis in the sagittal plane, frontal plane and transverse plane. Forty male runners were divided into two groups: twenty competitive runners and twenty recreational runners. A Vicon Motion System (Vicon Metrics Ltd., Oxford, UK) captured three-dimensional kinematics data during running at 3.3 m/s. The principal component analysis was used to determine the dominating variation in this model. Then, the principal component scores retained the first three principal components and were analyzed using independent t-tests. The recreational runners were found to have a smaller dorsiflexion angle, initial dorsiflexion contact angle, ankle inversion, knee adduction, range motion in the frontal knee plane and hip frontal plane. The running kinematics data were influenced by running experience. The findings from the study provide a better understanding of the kinematics variables for competitive and recreational runners. Thus, these findings might have implications for reducing running injury and improving running performance.

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 71%

Section: Participantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Competitive and Recreational Running Kinematics Examined Using Principal Components Analysis

Zhou

et al. 2021

Healthcare

Self Cite

“…Joint angles, joint moments, joint power, and joint work were normalized to the gait cycle over 101-time points. Ankle, knee and hip angles were calculated using Cardan angles in the sagittal plane (positiveflexion/dorsiflexion; negative-extension/plantarflexion) (Quan et al, 2021).…”

Section: Sagittal Plane Kinematicsmentioning

confidence: 99%

Effects of Fatigue Running on Joint Mechanics in Female Runners: A Prediction Study Based on a Partial Least Squares Algorithm

Front. Bioeng. Biotechnol.

Ren

et al. 2021

Self Cite

Background: Joint mechanics are permanently changed using different intensities and running durations. These variations in intensity and duration also influence fatigue during prolonged running. Little is known about the potential interactions between fatigue and joint mechanics in female recreational runners. Thus, the purpose of this study was to describe and examine kinematic and joint mechanical parameters when female recreational runners are subject to fatigue as a result of running.Method: Fifty female recreational runners maintained running on a treadmill to induce fatigue conditions. Joint mechanics, sagittal joint angle, moment, and power were recorded pre- and immediately post fatigue treadmill running.Result: Moderate reductions in absolute positive ankle power, total ankle energy dissipation, dorsiflexion at initial contact, max dorsiflexion angle, and range of motion of the joint ankle were collected after fatigue following prolonged fatigue running. Knee joint mechanics, joint angle, and joint power remained unchanged after prolonged fatigue running. Nevertheless, with the decreased ankle joint work, negative knee power increased. At the hip joint, the extension angle was significantly decreased. The range motion of the hip joint, hip positive work and hip positive power were increased during the post-prolonged fatigue running.Conclusion: This study found no proximal shift in knee joint mechanics in amateur female runners following prolonged fatigue running. The joint work redistribution was associated with running fatigue changes. As for long-distance running, runners should include muscle strength training to avoid the occurrence of running-related injuries.

Explaining the differences of gait patterns between high and low-mileage runners with machine learning

Zhou

et al. 2022

Sci Rep

Self Cite

Running gait patterns have implications for revealing the causes of injuries between higher-mileage runners and low-mileage runners. However, there is limited research on the possible relationships between running gait patterns and weekly running mileages. In recent years, machine learning algorithms have been used for pattern recognition and classification of gait features to emphasize the uniqueness of gait patterns. However, they all have a representative problem of being a black box that often lacks the interpretability of the predicted results of the classifier. Therefore, this study was conducted using a Deep Neural Network (DNN) model and Layer-wise Relevance Propagation (LRP) technology to investigate the differences in running gait patterns between higher-mileage runners and low-mileage runners. It was found that the ankle and knee provide considerable information to recognize gait features, especially in the sagittal and transverse planes. This may be the reason why high-mileage and low-mileage runners have different injury patterns due to their different gait patterns. The early stages of stance are very important in gait pattern recognition because the pattern contains effective information related to gait. The findings of the study noted that LRP completes a feasible interpretation of the predicted results of the model, thus providing more interesting insights and more effective information for analyzing gait patterns.