Computational modeling of running biomechanics in amateur runners - Teh, Yew Wei (2024)
This study was motivated by the need to understand the biomechanics of running, especially among amateur athletes, to enhance performance and prevent injuries. The research involved the development of a musculoskeletal model using OpenSim, focusing on the deep muscles of the lower limb. Experimental data were collected from ten amateur runners, and muscle-driven simulations were performed using techniques like Computed Muscle Control (CMC) and Static Optimization (SO). These simulations were compared to experimental data for validation, where Reduce Residual Algorithm (RRA) was found to be most effective in the determination of ankle moment. Statistically, the Root Mean Square Error (RMSE), correlation (r), in knee and ankle moments, under different types of shoe cushioning, no significant differences were found, with 0.3 kg/Nm of RMSE and approximately 95% of correlation in comparison with the experimental data. Thus, in this case, the computational time does become the key factor in evaluating them, where Inverse Kinematics (IK) was the best performed in simulating the knee and ankle joint moments in running motion under different types of hardness shoe cushioning, then followed by RRA and MocoTrack, which had the longest computational time respectively. On the other hand, focusing on muscle activations and joint moments during different running distances and with varying shoe cushioning, the results demonstrated that CMC provided the most accurate muscle force estimations, exhibiting the lowest root mean square error (RMSE) and highest correlation, though at the cost of increased computational time. Analysis revealed significant changes in muscle force generation at 80 km, indicating the body's adaptation to accumulated running distance. Muscles like the sartorius and semitendinosus exhibited compensatory force generation, while the adductor magnus ischial showed adaptive shifts between stance and swing phases. In conclusion, CMC provided the most accurate muscle force predictions. Based on the findings, running biomechanics can be better understood, aiding in improved training routines for amateur runners.