Sensitivity of Joint Kinematics and Kinetics to Different Pose Estimation Algorithms and Joint Constraints in the Elderly

Abstract

The purpose of this research was to study the sensitivity of lower limb joint kinematics and kinetics, calculated during different functional tasks (walking, stair descent and stair ascent) in a sample of older adults, to different pose estimation algorithms and models' joint constraints. Three models were developed and optimized differently: in one model, each segment had 6 degrees of freedom (segment optimization, SO), while in the other two, global optimization (GO) was used, with different joint constraints: (1) GO, allowing all joint rotations; (2) GOR, allowing three rotations at the hip, one at the knee (flexion/extension) and two at the ankle (dorsi/plantar flexion and eversion/inversion). The results showed that joint angles are more sensitive to the model's constraints than joint moments and, the more restrictive the model, the higher the differences between models, especially for the frontal and transverse planes (max. RMS difference during gait: 11.7 degrees (64%) vs 0.12 N·m/kg (35.4%). In addition, except for knee abduction/adduction angle, differences between SO and GO models were relatively low. Since GO avoids the nonanatomical dislocations sometimes observed in SO, choosing this model seems to be reasonable for future studies with a similar sample and study design.

Multibody Kinematics Optimization for the Estimation of Upper and Lower Limb Human Joint Kinematics: A Systematized Methodological Review

“…Joint constraints affect the resulting joint kinematics, especially the secondary (also called combined) DoFs like knee abduction or rotation [41,46,52,79]. Joint constraints also affect the location of the knee lateral and medial contact points [56].…”

“…Some features favoring MKO over SKO or nonoptimized kinematics were inter and intra-observer repeatability [16], robustness to marker mislocation [71], and avoidance of joint dislocations [45,61,79]. Most studies reinforced the conclusion that the effect of joint constraints is more pronounced on the secondary DoFs of gait [23,46,74]. Comparisons between constrained optimization and Kalman filtering showed that the benefit of Kalman filtering is not visible on the joint angle time histories but, unsurprisingly, on the velocities and accelerations [11,40].…”

“…Only eight studies evaluated the performance of MKO on a pathological population, namely patients with knee osteoarthritis [51,55], knee ligament deficiency [76,80], knee prosthesis [49,66], and cerebral palsy [75,78]. Pregnant [41,42], postmenopausal women [67] and elderly [46] were also studied.…”

Prediction of In Vivo Knee Joint Loads Using a Global Probabilistic Analysis

“…When the possible amount of an uncertainty is not available, the sensitivity of a particular output (e.g., joint kinematics, muscle moment arms, muscle function) to the change of an uncertain input (e.g., musculoskeletal geometry, musculotendon properties, joint axis location) can be quantified and provide valuable insights into the possible effects of lack of knowledge [5,16,17]. The impact of the uncertainty in different input parameters on several outputs of interest has been analyzed for musculotendon parameters [18][19][20][21][22], musculotendon geometry [23][24][25], joint center location [26], degree of freedom classification [27], joint models [28][29][30], skin marker placement [30], and pose estimation algorithms [31]. On the other hand, when the amount of possible uncertainty is known, an accurate evaluation of the output variability can be quantified.…”

“…While many studies have used probabilistic tools to analyze the effect of uncertain parameters on the output of interest [18][19][20][21][22][23][24][25][26][27][28][29][30][31], few of them have performed probabilistic analyses that combine multiple uncertainties belonging to different categories of parameters (described hereafter simply as "global") [36,37]. These global analyses allow a more complete investigation of the overall reliability of a model.…”