Statistical-Shape Prediction of Lower Limb Kinematics During Cycling, Squatting, Lunging, and Stepping—Are Bone Geometry Predictors Helpful?

Abstract

Purpose: Statistical shape methods have proven to be useful tools in providing statistical predications of several clinical and biomechanical features as to analyze and describe the possible link with them. In the present study, we aimed to explore and quantify the relationship between biometric features derived from imaging data and model-derived kinematics.Methods: Fifty-seven healthy males were gathered under strict exclusion criteria to ensure a sample representative of normal physiological conditions. MRI-based bone geometry was established and subject-specific musculoskeletal simulations in the Anybody Modeling System enabled us to derive personalized kinematics. Kinematic and shape findings were parameterized using principal component analysis. Partial least squares regression and canonical correlation analysis were then performed with the goal of predicting motion and exploring the possible association, respectively, with the given bone geometry. The relationship of hip flexion, abduction, and rotation, knee flexion, and ankle flexion with a subset of biometric features (age, length, and weight) was also investigated.Results: In the statistical kinematic models, mean accuracy errors ranged from 1.60° (race cycling) up to 3.10° (lunge). When imposing averaged kinematic waveforms, the reconstruction errors varied between 4.59° (step up) and 6.61° (lunge). A weak, yet clinical irrelevant, correlation between the modes describing bone geometry and kinematics was observed. Partial least square regression led to a minimal error reduction up to 0.42° compared to imposing gender-specific reference curves. The relationship between motion and the subject characteristics was even less pronounced with an error reduction up to 0.21°.Conclusion: The contribution of bone shape to model-derived joint kinematics appears to be relatively small and lack in clinical relevance.

Determining the relationship between tibiofemoral geometry and passive motion with partial least squares regression

“…The largest variation in the kinematics explained by geometry was in the first PLS component which accounted for 15% of the variation. De Roeck et al 18 found a weak relationship between the first component of a linear PLSR model, as in this study, of whole knee bone geometries that included the femur, tibia, and fibula and active motion with an explained kinematics variation of 1.4%-16.6% for a range of daily activities. In a data set of healthy and osteoarthritis participants, Lynch et al 16 similarly found variations in knee geometry were only weakly associated with kinematics of deep knee flexion with less than 50% of the variation in individual kinematic variables explained by geometry using a linear PCA, bivariate functional PCA, and random forest regression.…”

Virtual reality application for real-time pedalling cadence estimation based on hip ROM tracking with inertial sensors: a pilot study

“…The joint motions obtained (Figs. 6, 7) during standardized ergometer cycling conform to the hip normal range of motion investigated by different authors (Ericson et al 1988;De Roeck et al 2021). According to their studies, in the case of healthy young people the hip flexion moves between 32° and 70° of hip flexion (Ericson et al 1988), which implies a range of movement of approximately 40 • .…”

“…As 0 • or 320 • crank angle corresponds to the position when the pedal is in its top position and 180 • to the pedal bottom position. Therefore, it is expected to obtain a minimum hip flexion about 30 • and a maximum hip flexion about 70 • according to normal ranges of hip flexion during city bike cycling movement (De Roeck et al 2021).…”

Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing

“…Gait individuality research falls into either gait recognition (e.g., using deep learning) or into the biomechanics realm, where aspects of an individual's gait are used to quantify efficiency, symmetry, and comfort [3] , [4] , [5] . Consequently, past research has predicted an individual's gait kinematics over level ground by looking at anthropometric parameters, such as age, sex, BMI, and bone geometry [6] , [7] , [8] . As a person walks on more varied terrain, their gait fluctuates with respect to ground slope, load, and speed [1] , [3] .…”