Statistical Modeling of Lower Limb Kinetics During Deep Squat and Forward Lunge.

摘要: Purpose: Modern statistics and higher computational power have opened novel possibilities to complex data analysis. While gait has been the utmost described motion in quantitative human analysis, descriptions of more challenging movements like squat or lunge are currently lacking literature. The hip knee joints exposed high forces cause morbidity costs. Pre-surgical kinetic acquisition on a patient-specific anatomy is also scarce Studying normal inter-patient variability may lead other comparable studies initiate personalized therapies within orthopedics. Methods: Trials performed by 50 healthy young males who were not overweight approximately same age activity level. Spatial marker trajectories ground reaction force registrations imported into Anybody Modeling System based subject-specific geometry state-of-the-art TLEM 2.0 dataset. Hip joint obtained simulation with an inverse dynamics approach. With these forces, statistical model that accounts for inter-subject was created. For this, we applied principal component analysis order enable variance decomposition. This way, noise can be rejected still contemplate all waveform data, instead using deduced spatiotemporal parameters peak flexion stride length as done many analyses. In addition, this current paper is, authors' knowledge, first investigate generalization toward population. Results: Average range up 7.16 times body weight forwarded leg during lunge. Conversely, squat, load evenly distributed. both motions, reliable compact model, 12 modes 95.26% inter-individual population variance. maximal-depth 95.69% 14 modes. Model accuracies will increase when including components. Conclusion: Our design proved compact, accurate, reliable. models aimed at populations covering descriptive studies, sample size must least 50.