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

TL;DR

This paper introduces a hybrid LSTM-UKF framework that improves the accuracy and robustness of ankle angle and ground reaction force estimation during walking, aiding gait analysis and assistive device development.

Contribution

The study presents a novel hybrid LSTM-UKF model that outperforms standalone models in gait parameter prediction across different walking speeds.

Findings

Up to 18.6% lower RMSE for GRF prediction at 3 km/h.

UKF integration enhances robustness, reducing ankle angle RMSE by 22.4%.

Hybrid model performs reliably across subjects and walking conditions.

Abstract

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 $R^2$ 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.