Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking

Incorporating subject-specific features, such as alignment information, enhanced the accuracy of GRF predictions during walking. Complex pretrained models were outperformed by custom hybrid models for time-series 3D GRF prediction during walking. Custom models with lower computational demands and using alignment features are a more efficient and effective choice for applications requiring accurate and resource-efficient predictions.