Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Abstract

IntroductionGait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy.MethodsThe method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements.ResultsThe results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71–0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation.ConclusionTo conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.

Gait Deviations of the Uninvolved Limb and Their Significance in Unilateral Cerebral Palsy

“…These are commonly seen alterations of GRF in hemiplegic patients [21,31,32]. The reduced V-GRF during the late stance (particularly the second peak) and the prolonged stance phase of the uninvolved limb are especially shown to be a result of not being able to support the body weight and to unload the involved side [21,33].…”

Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation

“…Prior studies have combined physics-based models and optimization methods to estimate kinetics from IMU data [8]–[10] However, optimization methods require a relatively high computation time and depend on task-specific movement objectives (e.g., minimizing energy, maximizing speed) [11], [12]. Apart from physics-based models, other studies have attempted to employ deep learning models that directly map IMU data to kinetic parameters in an end-to-end manner [13]– [15].…”

“…Prior studies have combined physics-based models and optimization methods to estimate kinetics from IMU data [8]- [10]. However, optimization methods require a relatively high computation time and depend on task-specific movement objectives (e.g., minimizing energy, maximizing speed) [11], [12].…”

Ground Contact Force and Moment Estimation for Human–Exoskeleton Systems Using Dynamic Decoupled Coordinate System and Minimum Energy Hypothesis

“…However, the optimization process is still difficult due to the six additional unknown variables. In recent years, a musculoskeletal-modeling-based approach has been developed to predict external kinetics from kinematic data [9,30,31]. This kind of method requires a high-precision model of human muscle, bones, and soft tissue; it also requires the knowledge of the contact stiffness between the foot and the ground.…”