Researchers demonstrated that incorporating detailed knee alignment parameters into deep learning models significantly enhances the accuracy of predicting ground reaction forces during walking. The study evaluated 10 neural network architectures combining motion capture and accelerometer data, with most models benefiting from subject-specific biomechanical features except the LSTM model. This advancement offers improved individualization in biomechanical assessments, which can inform clinical diagnostics, rehabilitation planning, and wearable sensor technologies.