ArticleBioengineering (Basel, Switzerland)2025
Estimation of Three-Dimensional Ground Reaction Force and Center of Pressure During Walking Using a Machine-Learning-Based Markerless Motion Capture System.
Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Geometric Feature Relationship-Based Knowledge Distillation for Ground Reaction Force Estimation.Measurement : journal of the International Measurement Confederation · 2026Article
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- A comprehensive survey on diagnosis and assessment of Parkinson's disease via plantar pressure analysis.NPJ Parkinson's disease · 2026Article
- Biomechanics in Sport and Motion Analysis.Bioengineering (Basel, Switzerland) · 2026Article
- Synchronization of OpenCap with Force Platforms: Validation of an Event-Based Algorithm.Sensors (Basel, Switzerland) · 2026Article
- Tactile Sensor-Based Body Center of Pressure Estimation System Using Supervised Deep Learning Models.Sensors (Basel, Switzerland) · 2026Article
- Spatio-temporal graph convolutional networks with transfer learning for continuous ground reaction force estimation in hemiparetic gait.Frontiers in bioengineering and biotechnology · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
objectiveWe developed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) based on marker trajectories obtained from a markerless motion capture system.
methodsGait data were collected using two cameras and three force plates. Each gait dataset contained kinematic data and kinetic data from the stance phase. A multi-layer perceptron (MLP) and convolutional neural network (CNN) were constructed to estimate each component of GRF and COP based on the three-dimensional trajectories of the markers. A total of 100 samples were randomly selected as the test set, and the estimation performance was evaluated using the correlation coefficient (r) and relative root mean square error (rRMSE).
resultsThe r-values for MLP in each GRF component ranged from 0.918 to 0.989, with rRMSEs between 5.06% and 12.08%. The r-values for CNN in each GRF component ranged from 0.956 to 0.988, with rRMSEs between 6.03-9.44%. For the COP estimation, the r-values for MLP ranged from 0.727 to 0.982, with rRMSEs between 6.43% and 27.64%, while the r-values for CNN ranged from 0.896 to 0.977, with rRMSEs between 6.41% and 7.90%.
conclusionsIt is possible to estimate GRF and COP from markerless motion capture data. This approach provides an alternative method for measuring kinetic parameters without force plates during gait analysis.
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