A direct measurement of muscle and joint forces during typical human movements is desirable, e.g. to assess the effect of pain on these forces, and reduce joint forces to prevent further injury. For ethical and medical reasons, invasive joint force m
easurements are problematic, but computational models might provide a solution by predicting these forces. Since any modeling is an approximation, it is not yet clear how accurate predicted joint load forces and torques are for real-life biological movements. In contrast to real joints, it is, however possible to measure forces in implanted prostheses, providing an alternative method of validating the modelling approach. Therefore, the aim of this study was to investigate the accuracy of predicted forces in a knee joint during walking and squatting based on a computational musculoskeletal model, by comparing the model predictions with the corresponding real-life data gained from an instrumented knee prosthesis. Using calculated root mean squared error between the predicted and measured knee contact-forces, we found that musculoskeletal models can accurately predict knee joint forces. Furthermore, we demonstrated that the muscular coordination highly influences knee joint forces, as the knee joint forces were systematically reduced based on neuromuscular activation by -44% in walking and -15% in squatting. Our findings indicate that patients with a knee prosthesis may adapt their neuromuscular activation pattern to reduce joint forces during locomotion or everyday movements.
