Anterior knee pain is a commonly documented musculoskeletal disorder among badminton players. However, current biomechanical studies of badminton lunges mainly report kinetic profiles in the lower extremity with few investigations of in-vivo loadings. The objective of this study was to evaluate tissue loadings in the patellofemoral joint via musculoskeletal modelling and Finite Element simulation. The collected marker trajectories, ground reaction force and muscle activation data were used for musculoskeletal modelling to compute knee joint angles and quadricep muscle forces. These parameters were then set as boundary conditions and loads for a quasistatic simulation using the Abaqus Explicit solver. Simulations revealed that the left-forward (LF) and backward lunges showed greater contact pressure (14.98-29.61%) and von Mises stress (14.17-32.02%) than the right-forward and backward lunges; while, loadings in the left-backward lunge were greater than the left-forward lunge by 13-14%. Specifically, the stress in the chondral layer was greater than the contact interface, particularly in the patellar cartilage. These findings suggest that right-side dominant badminton players load higher in the right patellofemoral joint during left-side (backhand) lunges. Knowledge of these tissue loadings may provide implications for the training of badminton footwork, such as musculature development, to reduce cartilage loading accumulation, and prevent anterior knee pain.
Osteoarthritis (OA) is a commonly occurring cartilage degenerative disease. The end stage treatment is Total Knee Arthroplasty (TKA), which can be costly in terms of initial surgery, but also in terms of revision knee arthroplasty, which is quite often required. A novel conceptual knee implant has been proposed to function as a reducer of stress across the joint surface, to extend the period of time before TKA becomes necessary. The objective of this paper is to develop a computational model which can be used to assess the wear arising at the implant articulating surfaces. Experimental wear coefficients were determined from physical testing, the results of which were verified using a semi-analytical model. Experimental results were incorporated into an anatomically correct computational model of the knee and implant. The wear-rate predicted for the implant was 27.74 mm3 per million cycles (MC) and the wear depth predicted was 1.085 mm/MC. Whereas the wear-rate is comparable to that seen in conventional knee implants, the wear depth is significantly higher than for conventional knee prostheses, and indicates that, in order to be viable, wear-rates should be reduced in some way, perhaps by using low-wear polymers.
Ground reaction force (GRF) is a key metric in biomechanical research, including parameters of loading rate (LR), first impact peak, second impact peak, and transient between first and second impact peaks in heel strike runners. The GRFs vary over time during stance. This study was aimed to investigate the variances of GRFs in rearfoot striking runners across incremental speeds. Thirty female and male runners joined the running tests on the instrumented treadmill with speeds of 2.7, 3.0, 3.3, and 3.7 m/s. The discrete parameters of vertical average loading rate in the current study are consistent with the literature findings. The principal component analysis was modeled to investigate the main variances (95%) in the GRFs over stance. The females varied in the magnitude of braking and propulsive forces (PC1, 84.93%), whereas the male runners varied in the timing of propulsion (PC1, 53.38%). The female runners dominantly varied in the transient between the first and second peaks of vertical GRF (PC1, 36.52%) and LR (PC2, 33.76%), whereas the males variated in the LR and second peak of vertical GRF (PC1, 78.69%). Knowledge reported in the current study suggested the difference of the magnitude and patterns of GRF between male and female runners across different speeds. These findings may have implications for the prevention of sex-specific running-related injuries and could be integrated with wearable signals for the in-field prediction and estimation of impact loadings and GRFs.
Introduction: Playing badminton has been reported with extensive health benefits, while main injuries were documented in the lower extremity. This study was aimed to investigate and predict the knee- and ankle-joint loadings of athletes who play badminton, with "gold standard" facilities. The axial impact acceleration from wearables would be used to predict joint moments and contact forces during sub-maximal and maximal lunge footwork. Methods: A total of 25 badminton athletes participated in this study, following a previously established protocol of motion capture and musculoskeletal modelling techniques with the integration of a wearable inertial magnetic unit (IMU). We developed a principal component analysis (PCA) statistical model to extract features in the loading parameters and a multivariate partial least square regression (PLSR) machine learning model to correlate easily collected variables, such as the stance time, approaching velocity, and peak accelerations, with knee and ankle loading parameters (moments and contact forces). Results: The key variances of joint loadings were observed from statistical principal component analysis modelling. The promising accuracy of the partial least square regression model using input parameters was observed with a prediction accuracy of 94.52%, while further sensitivity analysis found a single variable from the ankle inertial magnetic unit that could predict an acceptable range (93%) of patterns and magnitudes of the knee and ankle loadings. Conclusion: The attachment of this single inertial magnetic unit sensor could be used to record and predict loading accumulation and distribution, and placement would exhibit less influence on the motions of the lower extremity. The intelligent prediction of loading patterns and accumulation could be integrated to design training and competition schemes in badminton or other court sports in a scientific manner, thus preventing fatigue, reducing loading-accumulation-related injury, and maximizing athletic performance.