ArticleJournal of orthopaedics2024
Prediction of joint moments from kinematics using machine learning in children with congenital talipes equino varus and typically developing peers.
Article in Journal of orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Electronic Systems for Monitoring Pediatric Gait Biomechanical Parameters: A Systematic Review of Embedded Technologies and Human-Machine Interfaces.Sensors (Basel, Switzerland) · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Understanding joint loading and the crucial role of joint moments is essential for developing treatment strategies in gait analysis, which often requires the precise estimation of joint moments through an inverse dynamic approach. This process necessitates the use of a force plate synchronized with a motion capture system. However, effectively capturing ground reaction force in typically developing (TD) children and those with congenital talipes equino varus (CTEV) presents challenges, while the availability and high cost of additional force plates pose additional challenges. Therefore the study aimed to develop, train, and identify the most effective machine learning (ML) model to predict joint moments from kinematics for TD children and those with CTEV. Method: In a study at the Gait Lab, 13 children with bilateral CTEV and 17 TD children underwent gait analysis to measure kinematics and kinetics, using a 12-camera Qualisys Motion Capture System and an AMTI force plate. ML models were then trained to predict joint moments from kinematic data as input. Results: The random forest regressor and deep neural networks (DNN) proved most effective in predicting joint moments from kinematics for TD children, yielding better results. The Random Forest regressor achieved an average r of 0.75 and nRMSE of 23.03 % for TD children, and r of 0.74 and 23.82 % for CTEV. DNN achieved an average r of 0.75 and nRMSE of 22.83 % for TD children, and r of 0.76 and nRMSE of 23.9 % for CTEV. Conclusions: The findings suggest that using machine learning to predict joint moments from kinematics shows moderate potential as an alternative to traditional gait analysis methods for both TD children and those with CTEV. Despite its potential, the current prediction accuracy limitations hinder the immediate clinical application of these techniques for decision-making in a pediatric population.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.