# Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation

arXiv: https://arxiv.org/abs/2601.06473 · HTML: https://mlsanity.com/paper/2601.06473/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 30 references that are in the map.
- First posted on arXiv 10 Jan 2026.

## Abstract (arXiv)

Accurate prediction of joint kinematics and kinetics is essential for advancing gait analysis and developing intelligent assistive systems such as prosthetics and exoskeletons. This study presents a hybrid LSTM-UKF framework for estimating ankle angle and ground reaction force (GRF) across varying walking speeds. A multimodal sensor fusion strategy integrates force plate data, knee angle, and GRF signals to enrich biomechanical context. Model performance was evaluated using RMSE and R2 under subject-specific validation. The LSTM-UKF consistently outperformed standalone LSTM and UKF models, achieving up to 18.6% lower RMSE for GRF prediction at 3 km/h. Additionally, UKF integration improved robustness, reducing ankle angle RMSE by up to 22.4% compared to UKF alone at 1 km/h. These results underscore the effectiveness of hybrid architectures for reliable gait prediction across subjects and walking conditions.