ArticleFrontiers in human neuroscience2023
Prediction of ground reaction forces and moments during walking in children with cerebral palsy.
Article in Frontiers in human neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Comparison of deep and conventional machine learning methods in predicting joint moments in patients with cerebral palsy.Medical & biological engineering & computing · 2026Article
- A predictive model of joint dynamics and ground reaction force using only leg length, body mass, and walking cadence.PloS one · 2026Article
- Toward future diagnostics of Parkinson's disease: a perspective on multimodal motor assessment and personalized digital twins.Frontiers in aging neuroscience · 2026Article
- Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.IEEE transactions on bio-medical engineering · 2024Article
- Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.bioRxiv : the preprint server for biology · 2024Article
- Ground Contact Force and Moment Estimation for Human-Exoskeleton Systems Using Dynamic Decoupled Coordinate System and Minimum Energy Hypothesis.Biomimetics (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Gait 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. Methods: The 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. Results: The 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. Conclusion: To 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.
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