Abstract
PurposeModern statistics and higher computational power have opened novel possibilities to complex data analysis. While gait has been the utmost described motion in quantitative human motion analysis, descriptions of more challenging movements like the squat or lunge are currently lacking in the literature. The hip and knee joints are exposed to high forces and cause high morbidity and costs. Pre-surgical kinetic data acquisition on a patient-specific anatomy is also scarce in the literature. Studying the normal inter-patient kinetic variability may lead to other comparable studies to initiate more personalized therapies within the orthopedics.MethodsTrials are performed by 50 healthy young males who were not overweight and approximately of the same age and activity level. Spatial marker trajectories and ground reaction force registrations are imported into the Anybody Modeling System based on subject-specific geometry and the state-of-the-art TLEM 2.0 dataset. Hip and knee joint reaction forces were obtained by a simulation with an inverse dynamics approach. With these forces, a statistical model that accounts for inter-subject variability was created. For this, we applied a principal component analysis in order to enable variance decomposition. This way, noise can be rejected and we still contemplate all waveform data, instead of using deduced spatiotemporal parameters like peak flexion or stride length as done in many gait analyses. In addition, this current paper is, to the authors’ knowledge, the first to investigate the generalization of a kinetic model data toward the population.ResultsAverage knee reaction forces range up to 7.16 times body weight for the forwarded leg during lunge. Conversely, during squat, the load is evenly distributed. For both motions, a reliable and compact statistical model was created. In the lunge model, the first 12 modes accounts for 95.26% of inter-individual population variance. For the maximal-depth squat, this was 95.69% for the first 14 modes. Model accuracies will increase when including more principal components.ConclusionOur model design was proved to be compact, accurate, and reliable. For models aimed at populations covering descriptive studies, the sample size must be at least 50.
Highlights
In biomechanics, the safety and efficiency of novel surgical techniques as well as the development of biocompatible products rely on its capability of being tested on humans through clinical trials
Lower limb kinetics can be estimated based on musculoskeletal models and ground force plates using inverse dynamics (Carbone et al, 2012; Galloway et al, 2012; Vaitkus and Várady, 2015; Bagwell et al, 2016)
Rank of roots measure suggests that seven principal components (PCs) are statistically significant in meaningfully describing the dataset, corresponding to 85% of data variance, whereas the equality of roots suggests that 14 PCs are to be included. *p < 0.05
Summary
The safety and efficiency of novel surgical techniques as well as the development of biocompatible products rely on its capability of being tested on humans through clinical trials. This fact is due to the high anatomical variability between individuals and the different functional activities, which have a significant effect in the ratio of the force components on the lower limb between subjects (Kutzner et al, 2010) and on the functional alignment of the prosthetic components of a lower limb implant (Smoger, 2016; Spencer-Gardner et al, 2016) Within this context, methodologies such as statistical models of the human anatomy as well as kinematics or kinetics that account for the anatomical inter-variability of the population combined with biomechanical simulation studies can provide non-invasive pre-surgical clinical output. The available literature lacks completeness as, to date, no study has considered a statistical model of the full lower limb, namely, on demanding tasks such as the deep squat and the forward lunge
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