When performing a biomechanical analysis of human movement, knowledge about the ground reaction force (GRF) is necessary to compute forces and moments within joints. This is important when analysing a movement and its effect on the human body. To obtain knowledge about the GRF, the gold standard is to use force plates which directly measure all three components of the GRF (mediolateral, anteroposterior and normal). However, force plates are heavy, clunky and expensive, setting constraints on possible experimental setups, which make it desirable to exclude them and instead use a predictive method to obtain the full GRF. Several predictive methods exist. The node model is a GRF predictive method included in a musculoskeletal modeling software. The tool use motion capture and virtual actuators to predict all three GRF components. However, this model has not yet been validated during weighted leg press and weighted squat. Furthermore, the normal component of the GRF can be measured continuously during the activity with pressure sensitive insoles (PSIs), which might provide better accuracy of the GRF prediction. The objectives of this thesis were to investigate whether force plates can be exluded during weighted leg press and weighted squat and to investigate whether PSIs can improve the GRF prediction. To investigate this, the node model and a developed shear model was validated. The shear model computes the two shear GRF components based on data from PSIs, an external load acting upon the body and data from a motion capture system. Both the node model and the shear model were analysed with two test subjects performing two successive repetitions of both weighted squat and weighted leg press in a flywheel exercise device. During the leg press exercise, the node model had a mean coeffcient of correlation (Pearson's) ranging from 0.70 to 0.98 for all three directions with a mean root mean square error ranging between 8 % to 20 % of the test person's body weight. The developed shear model had a coeffcient of correlation (Pearson's) between 0.64 to 0.99 and a mean root mean square error between 3 % and 21 % of the test person's body weight. This indicates that it is possible to exclude force plates and instead predict the GRF during weighted leg press. During squat, neither the node model nor the shear model provided accurate results regarding the mediolateral and anteroposterior components of the GRF, suggesting that force plates can not yet be excluded to obtain the full GRF during weighted squat. The results of the normal component during leg press was somewhat improved with the shear model compared to the node model, indicating that using PSIs can improve the results to some extent.

Aircraft design must be lightweight and cost-efficient on the condition of aircraft certification. In addition to standard load cases, human-induced loads can occur in the aircraft interior. These are crucial for optimal design but difficult to estimate. In this study, a simple bipedal spring-mass model with roller feet predicted human-induced loads caused by human gait for use within an end-to-end design process. The prediction needed no further experimental data. Gait movement and ground reaction force (GRF) were simulated by means of two parameter constraints with easily estimable input variables (gait speed, body mass, body height). To calibrate and validate the prediction model, experiments were conducted in which 12 test persons walked in an aircraft mock-up under different conditions. Additional statistical regression models helped to compensate for bipedal model limitations. Direct regression models predicted single GRF parameters as a reference without a bipedal model. The parameter constraint with equal gait speed in experiment and simulation yielded good estimates of force maxima (error 5.3%), while equal initial GRF gave a more reliable prediction. Both parameter constraints predicted contact time very well (error 0.9%). Predictions with the bipedal model including full GRF curves were overall as reliable as the reference.