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

Source: [Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation](<https://www.sota2.com/research/paper/6965d1616343fa78c83232f9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-14T05:00:04.658Z

Authors: Mundla Narasimhappa, Praveen Kumar

Published: 2026-01-10T07:53:07.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.06473>)

[PDF](<https://arxiv.org/pdf/2601.06473.pdf>)