No abstract

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.

No abstract

Falls have major consequences both at societal (health-care and economy) and individual (physical and psychological) levels. Questionnaires to assess fall risk are commonly used in the clinic, but their predictive value is limited. Objective methods, suitable for clinical application, are hence needed to obtain a quantitative assessment of individual fall risk. Falls in older adults often occur during walking and trunk position is known to play a critical role in balance control. Therefore, analysis of trunk kinematics during gait could present a viable approach to the development of such methods. In this study, nonlinear measures such as harmonic ratio (HR), index of harmonicity (IH), multiscale entropy (MSE) and recurrence quantification analysis (RQA) of trunk accelerations were calculated. These measures are not dependent on step detection, a potentially critical source of error. The aim of the present study was to investigate the association between the aforementioned measures and fall history in a large sample of subjects (42 fallers and 89 non - fallers) aged 50 or older. Univariate associations with fall history were found for MSE and RQA parameters in the AP direction; the best classification results were obtained for MSE with scale factor τ = 2 and for maximum length of diagonals in RQA (72.5% and 71% correct classifications, respectively). MSE and RQA were found to be positively associated with fall history and could hence represent useful tools in the identification of subjects for fall prevention programs.